Classification using Otto group dataset.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"In this notebook, we are going to explore various classification techniques using the Otto Group Product Challenge classification dataset.\n\nFrom machinelearningmastery:\n> This dataset describes the 93 obfuscated details of more than 61,000 products grouped into 10 product categories (e.g. fashion, electronics, etc.). Input attributes are counts of different events of some kind. The goal is to make predictions for new products as an array of probabilities for each of the 10 categories and models are evaluated using multiclass logarithmic loss (also called cross entropy).","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport seaborn as sns\nimport numpy as np\nimport pandas as pd\n\npd.set_option('display.max_columns', 100)\npd.set_option('display.max_rows', 50)\nsns.set_style('darkgrid')","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:43:58.160288Z","iopub.execute_input":"2021-06-08T12:43:58.160637Z","iopub.status.idle":"2021-06-08T12:43:58.753111Z","shell.execute_reply.started":"2021-06-08T12:43:58.160586Z","shell.execute_reply":"2021-06-08T12:43:58.752251Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"train = pd.read_csv('/kaggle/input/otto-group-product-classification-challenge/train.csv')\ntrain.head(7)","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:43:58.759690Z","iopub.execute_input":"2021-06-08T12:43:58.760052Z","iopub.status.idle":"2021-06-08T12:43:59.252368Z","shell.execute_reply.started":"2021-06-08T12:43:58.760014Z","shell.execute_reply":"2021-06-08T12:43:59.251456Z"},"trusted":true},"execution_count":2,"outputs":[{"execution_count":2,"output_type":"execute_result","data":{"text/plain":"   id  feat_1  feat_2  feat_3  feat_4  feat_5  feat_6  feat_7  feat_8  feat_9  \\\n0   1       1       0       0       0       0       0       0       0       0   \n1   2       0       0       0       0       0       0       0       1       0   \n2   3       0       0       0       0       0       0       0       1       0   \n3   4       1       0       0       1       6       1       5       0       0   \n4   5       0       0       0       0       0       0       0       0       0   \n5   6       2       1       0       0       7       0       0       0       0   \n6   7       2       0       0       0       0       0       0       2       0   \n\n   feat_10  feat_11  feat_12  feat_13  feat_14  feat_15  feat_16  feat_17  \\\n0        0        1        0        0        0        0        0        2   \n1        0        0        0        0        0        0        0        0   \n2        0        0        0        0        0        0        0        1   \n3        1        1        0        1        0        0        1        1   \n4        0        0        0        0        0        0        0        4   \n5        0        0        0        2        0        0        0        6   \n6        1        0        0        0        0        0        0        0   \n\n   feat_18  feat_19  feat_20  feat_21  feat_22  feat_23  feat_24  feat_25  \\\n0        0        0        0        0        1        0        4        1   \n1        2        0        0        0        0        0        0        0   \n2        0        0        0        0        0        0        0        0   \n3        0        0        0        0        0        0        7        2   \n4        0        0        0        0        0        0        1        0   \n5        0        0        2        0        0        0        5        0   \n6        0        0        0        0        1        0        0        1   \n\n   feat_26  feat_27  feat_28  feat_29  feat_30  feat_31  feat_32  feat_33  \\\n0        1        0        0        2        0        0        0        0   \n1        0        0        0        0        0        0        0        0   \n2        0        0        0        0        0        0        0        1   \n3        2        0        0        0       58        0       10        0   \n4        0        0        0        0        0        0        0        0   \n5        0        0        0        1        0        0        2        0   \n6        0        0        0        2        2        0        0        0   \n\n   feat_34  feat_35  feat_36  feat_37  feat_38  feat_39  feat_40  feat_41  \\\n0        0        1        0        0        0        0        1        0   \n1        0        0        0        1        0        0        0        0   \n2        0        0        0        0        0        0        0        0   \n3        0        0        0        0        3        0        0        0   \n4        0        0        0        0        0        0        0        1   \n5        0        0        2        0        0        0        1        0   \n6        0        0        0        1        0        2        0        0   \n\n   feat_42  feat_43  feat_44  feat_45  feat_46  feat_47  feat_48  feat_49  \\\n0        5        0        0        0        0        0        2        0   \n1        0        0        0        0        0        0        0        0   \n2        0        0        0        0        0        0        1        0   \n3        0        0        2        0        2        0        1        2   \n4        0        0        0        0        0        0        0        0   \n5        0        0        0        0        0        2        2        0   \n6        0        0        0        0        0        0        0        0   \n\n   feat_50  feat_51  feat_52  feat_53  feat_54  feat_55  feat_56  feat_57  \\\n0        0        0        0        0        1        0        0        2   \n1        0        0        0        0        0        0        0        0   \n2        0        0        0        0        0        1        0        0   \n3        1        3        0        0        3        1        0        0   \n4        2        0        0        0        0        0        0        0   \n5        0        0        0        0        3        0        0        0   \n6        0        0        0        2        0        2        0        0   \n\n   feat_58  feat_59  feat_60  feat_61  feat_62  feat_63  feat_64  feat_65  \\\n0        0        0       11        0        1        1        0        1   \n1        1        0        0        0        0        0        1        0   \n2        0        0        0        0        0        0        0        0   \n3        0        0        0        0        0        0        0        2   \n4        0        0        0        0        1        0        0        0   \n5        0        0        5        0        1        0        1        4   \n6        0        0        2        0        1        0        3        1   \n\n   feat_66  feat_67  feat_68  feat_69  feat_70  feat_71  feat_72  feat_73  \\\n0        0        7        0        0        0        1        0        0   \n1        0        1        0        0        0        0        0        2   \n2        0        6        0        0        2        0        0        0   \n3        1        5        0        0        4        0        0        2   \n4        0        0        1        0        0        3        0        0   \n5        2        6        0        2        4        2        0        0   \n6        0        1        0        0        0        3        0        0   \n\n   feat_74  feat_75  feat_76  feat_77  feat_78  feat_79  feat_80  feat_81  \\\n0        0        0        0        0        0        2        1        0   \n1        1        0        1        0        1        0        0        0   \n2        0        0        0        0        0        0        0        1   \n3        1        0        1        0        0        1        1        2   \n4        0        0        0        0        0        4        0        1   \n5        1        0        2        0        4        3        0        0   \n6        0        0        0        0        0        0        0        0   \n\n   feat_82  feat_83  feat_84  feat_85  feat_86  feat_87  feat_88  feat_89  \\\n0        0        0        0        1        0        0        0        0   \n1        0        0        0        0        0        0        0        0   \n2        0        0        0        0        0        0        0        0   \n3        2        0       22        0        1        2        0        0   \n4        0        0        0        1        0        0        0        0   \n5        0        0        1        0        3        0        0        0   \n6        0        0        0        1        1        0        0        0   \n\n   feat_90  feat_91  feat_92  feat_93   target  \n0        0        0        0        0  Class_1  \n1        0        0        0        0  Class_1  \n2        0        0        0        0  Class_1  \n3        0        0        0        0  Class_1  \n4        1        0        0        0  Class_1  \n5        0        2        0        0  Class_1  \n6        0        0        0        1  Class_1  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>feat_1</th>\n      <th>feat_2</th>\n      <th>feat_3</th>\n      <th>feat_4</th>\n      <th>feat_5</th>\n      <th>feat_6</th>\n      <th>feat_7</th>\n      <th>feat_8</th>\n      <th>feat_9</th>\n      <th>feat_10</th>\n      <th>feat_11</th>\n      <th>feat_12</th>\n      <th>feat_13</th>\n      <th>feat_14</th>\n      <th>feat_15</th>\n      <th>feat_16</th>\n      <th>feat_17</th>\n      <th>feat_18</th>\n      <th>feat_19</th>\n      <th>feat_20</th>\n      <th>feat_21</th>\n      <th>feat_22</th>\n      <th>feat_23</th>\n      <th>feat_24</th>\n      <th>feat_25</th>\n      <th>feat_26</th>\n      <th>feat_27</th>\n      <th>feat_28</th>\n      <th>feat_29</th>\n      <th>feat_30</th>\n      <th>feat_31</th>\n      <th>feat_32</th>\n      <th>feat_33</th>\n      <th>feat_34</th>\n      <th>feat_35</th>\n      <th>feat_36</th>\n      <th>feat_37</th>\n      <th>feat_38</th>\n      <th>feat_39</th>\n      <th>feat_40</th>\n      <th>feat_41</th>\n      <th>feat_42</th>\n      <th>feat_43</th>\n      <th>feat_44</th>\n      <th>feat_45</th>\n      <th>feat_46</th>\n      <th>feat_47</th>\n      <th>feat_48</th>\n      <th>feat_49</th>\n      <th>feat_50</th>\n      <th>feat_51</th>\n      <th>feat_52</th>\n      <th>feat_53</th>\n      <th>feat_54</th>\n      <th>feat_55</th>\n      <th>feat_56</th>\n      <th>feat_57</th>\n      <th>feat_58</th>\n      <th>feat_59</th>\n      <th>feat_60</th>\n      <th>feat_61</th>\n      <th>feat_62</th>\n      <th>feat_63</th>\n      <th>feat_64</th>\n      <th>feat_65</th>\n      <th>feat_66</th>\n      <th>feat_67</th>\n      <th>feat_68</th>\n      <th>feat_69</th>\n      <th>feat_70</th>\n      <th>feat_71</th>\n      <th>feat_72</th>\n      <th>feat_73</th>\n      <th>feat_74</th>\n      <th>feat_75</th>\n      <th>feat_76</th>\n      <th>feat_77</th>\n      <th>feat_78</th>\n      <th>feat_79</th>\n      <th>feat_80</th>\n      <th>feat_81</th>\n      <th>feat_82</th>\n      <th>feat_83</th>\n      <th>feat_84</th>\n      <th>feat_85</th>\n      <th>feat_86</th>\n      <th>feat_87</th>\n      <th>feat_88</th>\n      <th>feat_89</th>\n      <th>feat_90</th>\n      <th>feat_91</th>\n      <th>feat_92</th>\n      <th>feat_93</th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>11</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>7</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Class_1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Class_1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>6</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Class_1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>6</td>\n      <td>1</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>7</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>58</td>\n      <td>0</td>\n      <td>10</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>22</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Class_1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Class_1</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>6</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>7</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>6</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>4</td>\n      <td>2</td>\n      <td>6</td>\n      <td>0</td>\n      <td>2</td>\n      <td>4</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>4</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Class_1</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>7</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Class_1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"test = pd.read_csv('/kaggle/input/otto-group-product-classification-challenge/test.csv')\ntest.head(7)","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:43:59.254068Z","iopub.execute_input":"2021-06-08T12:43:59.254431Z","iopub.status.idle":"2021-06-08T12:44:00.239845Z","shell.execute_reply.started":"2021-06-08T12:43:59.254391Z","shell.execute_reply":"2021-06-08T12:44:00.239008Z"},"trusted":true},"execution_count":3,"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"   id  feat_1  feat_2  feat_3  feat_4  feat_5  feat_6  feat_7  feat_8  feat_9  \\\n0   1       0       0       0       0       0       0       0       0       0   \n1   2       2       2      14      16       0       0       0       0       0   \n2   3       0       1      12       1       0       0       0       0       0   \n3   4       0       0       0       1       0       0       0       0       0   \n4   5       1       0       0       1       0       0       1       2       0   \n5   6       0       0       0       0       0       0       0       0      17   \n6   7       0       0       0       0       0       0       0       0       0   \n\n   feat_10  feat_11  feat_12  feat_13  feat_14  feat_15  feat_16  feat_17  \\\n0        3        0        0        0        3        2        1        0   \n1        0        0        0        0        0        2        2        0   \n2        0        7        1        0        0        0        7        0   \n3        0        0        0        0        0       21        3        0   \n4        3        0        0        0        0        0        0        0   \n5        0        0        0        0        0        0        0        0   \n6        5        0        0        0        1        0        4        0   \n\n   feat_18  feat_19  feat_20  feat_21  feat_22  feat_23  feat_24  feat_25  \\\n0        0        0        0        0        0        0        5        3   \n1        0        0        0        0        2        0        4        0   \n2        2        0        0        0        4        0        0        0   \n3        0        0        0        0        0        0        0        4   \n4        0        0        0        0        0        0        4        0   \n5        0        0        0        0        0        0        4        2   \n6        1        0        4        0        2        0        0        1   \n\n   feat_26  feat_27  feat_28  feat_29  feat_30  feat_31  feat_32  feat_33  \\\n0        1        1        0        0        0        0        0        1   \n1        4        0        0        0        0        2        0        0   \n2        1        1        2        0        0        0        0        0   \n3        0        0        0        1        0        0        0        2   \n4        0        0        0        0        3        0        1        0   \n5        0        0        0        0        0        0        6        0   \n6        0        0        1        0        0        1        0        0   \n\n   feat_34  feat_35  feat_36  feat_37  feat_38  feat_39  feat_40  feat_41  \\\n0        0        0        1        0        1        0        1        0   \n1        0        8        0        0        0        0        0        0   \n2        0        1        0        0        2        0        0        0   \n3        0        0        0        0        1        0        0        2   \n4        1        0        0        0        0        0        0        0   \n5        0        0        0        0        0        0        0        0   \n6        0        2        0        2        1        1        0        1   \n\n   feat_42  feat_43  feat_44  feat_45  feat_46  feat_47  feat_48  feat_49  \\\n0        0        0        0        0        0        0        0        0   \n1        0        0        2        0        4        0        4        0   \n2        1        0        1        0        4        0        2        3   \n3        0        0        0        0        0        0        0        0   \n4        0        0        0        0        0        0        3        1   \n5        0        0        0        0        0        0        8        0   \n6        0        0        2        0        0        0        0        0   \n\n   feat_50  feat_51  feat_52  feat_53  feat_54  feat_55  feat_56  feat_57  \\\n0        0        0        0        0        0        0        0        0   \n1        0        0        0        0       24        0        0        0   \n2        0        0        0        0       10        1        0        2   \n3        0        0        0        0        2        0        0        0   \n4        0        0        0        0        0        0        0        0   \n5        0        0        0        0        0        0        0        0   \n6        2        0        3        0        1        0        1        1   \n\n   feat_58  feat_59  feat_60  feat_61  feat_62  feat_63  feat_64  feat_65  \\\n0        0        0        0        0        0        0        3        0   \n1        0        0        0        0        0        0        6        8   \n2        0        0        1        6        1        1        0        0   \n3        0        0        0        0        9        0        0        2   \n4        0        0        0        0        0        0        0        0   \n5        0        0        0        0        0        0       11        0   \n6        0        1        0        0        2        0        0        2   \n\n   feat_66  feat_67  feat_68  feat_69  feat_70  feat_71  feat_72  feat_73  \\\n0        0        0        0        1        1        0        1        0   \n1        0        0        0        0        0        0        0        0   \n2        1        1        1        2        0        0        2        0   \n3        0        0        0        0        6        0        8        0   \n4        0        7        0        0        0        0        0        4   \n5        0        7        0        0        0        1        0        1   \n6        0        0        0        2        2        0        1        0   \n\n   feat_74  feat_75  feat_76  feat_77  feat_78  feat_79  feat_80  feat_81  \\\n0        0        0        0        0        0        0        0        0   \n1        0        0        0        2        0        0        4        0   \n2        0        0        0        0        0        0        6        0   \n3        0        0        0        1        0        0        0        0   \n4        0        5       16        0        0        0        0        0   \n5        0        0        0        0        0        0        0        0   \n6        0        0        0        0        0        0        0        0   \n\n   feat_82  feat_83  feat_84  feat_85  feat_86  feat_87  feat_88  feat_89  \\\n0        0        0        0        0       11        1       20        0   \n1        2        0        0        0        0        0        0        4   \n2        2        0        0        0        0        0        2        0   \n3        0        0        0        3        1        0        0        0   \n4        0        1        0        0        0        0        0        0   \n5        0        0        0        0        1        1        1        0   \n6        0        2        0        3        0        0        1        0   \n\n   feat_90  feat_91  feat_92  feat_93  \n0        0        0        0        0  \n1        0        0        2        0  \n2        0        0        0        1  \n3        0        0        0        0  \n4        0        9        0        0  \n5        0        0        0        0  \n6       18        1        0        0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>feat_1</th>\n      <th>feat_2</th>\n      <th>feat_3</th>\n      <th>feat_4</th>\n      <th>feat_5</th>\n      <th>feat_6</th>\n      <th>feat_7</th>\n      <th>feat_8</th>\n      <th>feat_9</th>\n      <th>feat_10</th>\n      <th>feat_11</th>\n      <th>feat_12</th>\n      <th>feat_13</th>\n      <th>feat_14</th>\n      <th>feat_15</th>\n      <th>feat_16</th>\n      <th>feat_17</th>\n      <th>feat_18</th>\n      <th>feat_19</th>\n      <th>feat_20</th>\n      <th>feat_21</th>\n      <th>feat_22</th>\n      <th>feat_23</th>\n      <th>feat_24</th>\n      <th>feat_25</th>\n      <th>feat_26</th>\n      <th>feat_27</th>\n      <th>feat_28</th>\n      <th>feat_29</th>\n      <th>feat_30</th>\n      <th>feat_31</th>\n      <th>feat_32</th>\n      <th>feat_33</th>\n      <th>feat_34</th>\n      <th>feat_35</th>\n      <th>feat_36</th>\n      <th>feat_37</th>\n      <th>feat_38</th>\n      <th>feat_39</th>\n      <th>feat_40</th>\n      <th>feat_41</th>\n      <th>feat_42</th>\n      <th>feat_43</th>\n      <th>feat_44</th>\n      <th>feat_45</th>\n      <th>feat_46</th>\n      <th>feat_47</th>\n      <th>feat_48</th>\n      <th>feat_49</th>\n      <th>feat_50</th>\n      <th>feat_51</th>\n      <th>feat_52</th>\n      <th>feat_53</th>\n      <th>feat_54</th>\n      <th>feat_55</th>\n      <th>feat_56</th>\n      <th>feat_57</th>\n      <th>feat_58</th>\n      <th>feat_59</th>\n      <th>feat_60</th>\n      <th>feat_61</th>\n      <th>feat_62</th>\n      <th>feat_63</th>\n      <th>feat_64</th>\n      <th>feat_65</th>\n      <th>feat_66</th>\n      <th>feat_67</th>\n      <th>feat_68</th>\n      <th>feat_69</th>\n      <th>feat_70</th>\n      <th>feat_71</th>\n      <th>feat_72</th>\n      <th>feat_73</th>\n      <th>feat_74</th>\n      <th>feat_75</th>\n      <th>feat_76</th>\n      <th>feat_77</th>\n      <th>feat_78</th>\n      <th>feat_79</th>\n      <th>feat_80</th>\n      <th>feat_81</th>\n      <th>feat_82</th>\n      <th>feat_83</th>\n      <th>feat_84</th>\n      <th>feat_85</th>\n      <th>feat_86</th>\n      <th>feat_87</th>\n      <th>feat_88</th>\n      <th>feat_89</th>\n      <th>feat_90</th>\n      <th>feat_91</th>\n      <th>feat_92</th>\n      <th>feat_93</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>11</td>\n      <td>1</td>\n      <td>20</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>14</td>\n      <td>16</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>8</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>24</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>6</td>\n      <td>8</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>12</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>7</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>7</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>2</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>10</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>6</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>6</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>21</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>9</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>6</td>\n      <td>0</td>\n      <td>8</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>7</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>5</td>\n      <td>16</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>9</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>6</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>17</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>6</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>8</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>11</td>\n      <td>0</td>\n      <td>0</td>\n      <td>7</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>7</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>18</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"The following code cell shows that we have a class imbalance in the target column of the `train` dataset.","metadata":{}},{"cell_type":"code","source":"sns.countplot(x = train.target)","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:44:00.241110Z","iopub.execute_input":"2021-06-08T12:44:00.241455Z","iopub.status.idle":"2021-06-08T12:44:00.490594Z","shell.execute_reply.started":"2021-06-08T12:44:00.241418Z","shell.execute_reply":"2021-06-08T12:44:00.489623Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:xlabel='target', ylabel='count'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAZEAAAEHCAYAAABvHnsJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAAAmdklEQVR4nO3de3RU5b3/8feQmyhJBjCZEU3pERFYKMH+ishKhHZgEiAJhED01GIl3ipQMNBiDRwjEC5CsVDNKoeciNeq5ZagROUyCElaEC1iBMF1cg5ZBA+ZUMgFEEgy7N8f/JhfcyQYdpmZhHxea7lW8syeeb7PMzt+2HeLYRgGIiIiJnQKdAEiItJ+KURERMQ0hYiIiJimEBEREdMUIiIiYlpwoAvwtwsXLuDx6IQ0EZGrERISdNn2DhciHo9Bbe23gS5DRKRdiYoKv2y7dmeJiIhpChERETFNISIiIqYpRERExDSFiIiImKYQERER0xQiIiJims9CJCsriyFDhpCcnNys/c0332TkyJEkJSWxdOlSb/uqVatwOp0kJiZSUlLibS8uLiYxMRGn00leXp63vbKykvT0dJxOJ5mZmTQ0NPhqKCIi0gKfhUhaWhr5+fnN2nbv3o3L5eK9996jqKiIxx57DIDy8nKKioooKioiPz+fefPm4fF48Hg8zJ8/n/z8fIqKiti0aRPl5eUALFu2jEmTJrF161YiIiJYt26dr4YiIiIt8NkV64MGDeLo0aPN2t555x2efPJJQkNDAejevTsALpeLpKQkQkNDiYmJoWfPnpSVlQHQs2dPYmJiAEhKSsLlctGrVy92797Niy++CMC4cePIzc3loYce8tVw/KpbZAhBoTf4tU9PwzlO1jX6tU8Raf/8etuTiooKPvvsM5YvX05YWBjPPPMMAwYMwO12Exsb613OZrPhdrsBsNvtzdrLysqoqakhIiKC4OBg7zKXlv8+QUEWrNYbr+Gorr2gkCCOzL/br33+IPtLrNYQv/YpIu2fX0PE4/FQV1fHmjVr+PLLL8nMzMTlcvmzhHZx76yW7lHja219XkQkcFr6/5JfQ8Rms+F0OrFYLAwYMIBOnTpRU1ODzWajqqrKu5zb7cZmswFctr1r167U19fT1NREcHAwVVVV3uVFRMR//HqK74gRI/jkk08AOHz4MI2NjXTt2hWHw0FRURENDQ1UVlZSUVHBgAEDuPvuu6moqKCyspKGhgaKiopwOBxYLBYGDx7M5s2bASgoKMDhcPhzKCIigg+3RGbOnMmePXuoqalh6NChTJs2jfHjxzN79mySk5MJCQnhhRdewGKx0Lt3b0aNGsXo0aMJCgoiOzuboKCL967Pzs7m8ccfx+PxMH78eHr37g3ArFmzmDFjBitWrKBfv36kp6f7aigiItICi2EYHeoJTY2Nnja/7z8qKjwgB9aPHz/l1z5FpP3Q80REROSaU4iIiIhpChERETFNISIiIqYpRERExDSFiIiImKYQERER0xQiIiJimkJERERMU4iIiIhpChERETFNISIiIqYpRERExDSFiIiImKYQERER0xQiIiJimkJERERM81mIZGVlMWTIEJKTk7/z2urVq+nTpw8nT54EwDAMFixYgNPpJCUlhQMHDniXLSgoICEhgYSEBAoKCrzt+/fvJyUlBafTyYIFC+hgD2gUEWkTfPaM9bS0NCZOnMhvf/vbZu3Hjh3jL3/5Cz169PC2FRcXU1FRwZYtW/jiiy+YO3cua9eupba2ltzcXNavX4/FYiEtLQ2Hw0FkZCRz584lJyeH2NhYnnjiCYqLixk2bJivhiPSbkWGhxJ6Q5jf+204d566Uw1+71f8y2chMmjQII4ePfqd9sWLFzNr1iymTJnibXO5XKSmpmKxWBg4cCD19fVUV1ezZ88e4uLisFqtAMTFxVFSUsK9997L6dOnGThwIACpqam4XC6FiMhlhN4QxsKJE/ze75y31oFC5LrnsxC5nG3bthEdHU3fvn2btbvdbux2u/d3u92O2+3+TrvNZrts+6XlWyMoyILVeuM/OZLrk+ZFrjWtU9c/v4XI2bNnWbVqFatXr/ZXl5fl8RjU1n4b0Bq+T1RUeED6bevzIuYEan0CrVPXk5bWI7+dnXXkyBGOHj3K2LFjcTgcVFVVkZaWxvHjx7HZbFRVVXmXraqqwmazfafd7XZftv3S8iIi4l9+C5E+ffqwa9cutm/fzvbt27Hb7WzYsIGoqCgcDgeFhYUYhsG+ffsIDw8nOjqa+Ph4SktLqauro66ujtLSUuLj44mOjqZLly7s27cPwzAoLCxk+PDh/hqKiIj8Pz7bnTVz5kz27NlDTU0NQ4cOZdq0aaSnp1922WHDhrFz506cTiedO3dm0aJFAFitVqZMmcKECRcPCk6dOtV7kP35558nKyuLc+fOMXToUIYOHeqroYiISAssRge7wKKx0dPm99NGRYVzZP7dfu3zB9lfcvz4Kb/2Kf4RFRUesLOztE5dPwJ+TERERK4/ChERETFNISIiIqYpRERExDSFiIiImKYQERER0xQiIiJimkJERERMU4iIiIhpChERETFNISIiIqYpRERExDSFiIiImKYQERER0xQiIiJimkJERERMU4iIiIhpPguRrKwshgwZQnJysrdtyZIljBw5kpSUFKZOnUp9fb33tVWrVuF0OklMTKSkpMTbXlxcTGJiIk6nk7y8PG97ZWUl6enpOJ1OMjMzaWho8NVQRESkBT4LkbS0NPLz85u1xcXFsWnTJt5//31++MMfsmrVKgDKy8spKiqiqKiI/Px85s2bh8fjwePxMH/+fPLz8ykqKmLTpk2Ul5cDsGzZMiZNmsTWrVuJiIhg3bp1vhqKiIi0wGchMmjQICIjI5u1xcfHExwcDMDAgQOpqqoCwOVykZSURGhoKDExMfTs2ZOysjLKysro2bMnMTExhIaGkpSUhMvlwjAMdu/eTWJiIgDjxo3D5XL5aigiItKC4EB1vH79ekaNGgWA2+0mNjbW+5rNZsPtdgNgt9ubtZeVlVFTU0NERIQ3kOx2u3f57xMUZMFqvfFaDeO6onmRa03r1PUvICGycuVKgoKCGDNmjN/79ngMamu/9Xu/VyMqKjwg/bb1eRFzArU+gdap60lL65HfQ2TDhg3s2LGD1157DYvFAlzcwri0awsubpnYbDaAy7Z37dqV+vp6mpqaCA4Opqqqyru8iIj4j19P8S0uLiY/P5+VK1fSuXNnb7vD4aCoqIiGhgYqKyupqKhgwIAB3H333VRUVFBZWUlDQwNFRUU4HA4sFguDBw9m8+bNABQUFOBwOPw5FBERwYdbIjNnzmTPnj3U1NQwdOhQpk2bRl5eHg0NDWRkZAAQGxvL/Pnz6d27N6NGjWL06NEEBQWRnZ1NUFAQANnZ2Tz++ON4PB7Gjx9P7969AZg1axYzZsxgxYoV9OvXj/T0dF8NRUREWmAxDMMIdBH+1NjoafP7aaOiwjky/26/9vmD7C85fvyUX/sU/4iKCmfhxAl+73fOW+u0Tl1HWjomoivWRUTENIWIiIiYphARERHTFCIiImKaQkRERExTiIiIiGkKERERMU0hIiIipilERETENIWIiIiYphARERHTFCIiImKaQkRERExTiIiIiGkKERERMU0hIiIipvksRLKyshgyZAjJycnettraWjIyMkhISCAjI4O6ujoADMNgwYIFOJ1OUlJSOHDggPc9BQUFJCQkkJCQQEFBgbd9//79pKSk4HQ6WbBgAR3s2VoiIm2Cz0IkLS2N/Pz8Zm15eXkMGTKELVu2MGTIEPLy8oCLz16vqKhgy5Yt5OTkMHfuXOBi6OTm5rJmzRrWrl1Lbm6uN3jmzp1LTk4OW7ZsoaKiguLiYl8NRUREWuCzEBk0aBCRkZHN2lwuF6mpqQCkpqaybdu2Zu0Wi4WBAwdSX19PdXU1paWlxMXFYbVaiYyMJC4ujpKSEqqrqzl9+jQDBw7EYrGQmpqKy+Xy1VBERKQFwf7s7MSJE0RHRwMQFRXFiRMnAHC73djtdu9ydrsdt9v9nXabzXbZ9kvLt0ZQkAWr9cZrMZzrjuZFrjWtU9c/v4bIP7JYLFgsFr/36/EY1NZ+6/d+r0ZUVHhA+m3r8yLmBGp9Aq1T15OW1iO/np3VvXt3qqurAaiurqZbt27AxS2Mqqoq73JVVVXYbLbvtLvd7su2X1peRET8y68h4nA4KCwsBKCwsJDhw4c3azcMg3379hEeHk50dDTx8fGUlpZSV1dHXV0dpaWlxMfHEx0dTZcuXdi3bx+GYTT7LBER8R+f7c6aOXMme/bsoaamhqFDhzJt2jSefPJJMjMzWbduHT169GDFihUADBs2jJ07d+J0OuncuTOLFi0CwGq1MmXKFCZMmADA1KlTsVqtADz//PNkZWVx7tw5hg4dytChQ301FBERaYHF6GAXWDQ2etr8ftqoqHCOzL/br33+IPtLjh8/5dc+xT+iosJZOHGC3/ud89Y6rVPXkTZxTERERK4vChERETFNISIiIqa1KkQeeeSRVrWJiEjHcsWzs86fP8/Zs2epqamhrq7Oe5PD06dPt/oKcRERuX5dMUTeffddXn/9daqrq0lLS/OGSJcuXZg4caJfChQRkbbriiHyyCOP8Mgjj/Dmm2/y8MMP+6smERFpJ1p1seHDDz/M3r17+eabb/B4PN72S3fkFRGRjqlVITJr1iwqKyvp27cvQUFBAN5bsIuISMfVqhDZv38/H3zwQUDuuisiIm1Xq07x7d27N8ePH/d1LSIi0s60akukpqaGpKQkBgwYQEhIiLf93//9331WmIiItH2tCpFp06b5ug4REWmHWhUi9957r6/rEBGRdqhVIXLPPfd4D6o3NjbS1NRE586d2bt3r0+LExHxp8jIMEJDQ/3eb0NDA3V15/3e77XQqhD5/PPPvT8bhoHL5WLfvn2+qklEJCBCQ0OZO3eu3/u92Gf7DJGrvouvxWJhxIgRlJaW+qIeERFpR1q1JbJlyxbvzxcuXGD//v2EhYWZ7vS1115j7dq1WCwW7rzzThYvXkx1dTUzZ86ktraW/v37s3TpUkJDQ2loaOCZZ57hwIEDWK1Wli9fzm233QbAqlWrWLduHZ06deLf/u3fuP/++03XJCIiV69VWyIff/yx97/S0lJuuukm/vjHP5rq0O1288Ybb7B+/Xo2bdqEx+OhqKiIZcuWMWnSJLZu3UpERATr1q0DYO3atURERLB161YmTZrEsmXLACgvL6eoqIiioiLy8/OZN29es1uyiIiI77VqS2Tx4sXXtFOPx8O5c+cIDg7m3LlzREVFsXv3bl588UUAxo0bR25uLg899BDbt2/nV7/6FQCJiYnMnz/fe1wmKSmJ0NBQYmJi6NmzJ2VlZdxzzz3XtFYREWlZq0KkqqqKnJwc79lYP/7xj5kzZw52u/2qO7TZbDz66KP89Kc/JSwsjLi4OPr3709ERATBwRfLsdvt3ueVuN1ubrnllovFBgcTHh5OTU0Nbreb2NjYZp/bmmecBAVZsFpvvOq6O4IrzcsFSxNhweZ3YZp1vuk8nYxWrabSBulvrfXa61y16q8zKyuL5ORk/vCHPwDw3nvvkZWVxauvvnrVHdbV1eFyuXC5XISHh/P0009TUlJy1Z9jlsdjUFv7rd/6MyMqKjwg/V5pXqKiwol7Oc6P1Vz0l2l/4fjxU37v93oSqPUJrrxOtUWaq5a1NDetOiZy8uRJxo8fT3BwMMHBwaSlpXHy5ElThfz1r3/ltttuo1u3boSEhJCQkMDevXupr6+nqakJuLjlY7PZgItbGMeOHQOgqamJU6dO0bVrV2w2G1VVVd7Pdbvd3veIiIh/tCpErFYrGzduxOPx4PF42LhxI1ar1VSHPXr04IsvvuDs2bMYhsGuXbu44447GDx4MJs3bwagoKAAh8MBgMPhoKCgAIDNmzdz3333YbFYcDgcFBUV0dDQQGVlJRUVFQwYMMBUTSIiYk6rdmctWrSInJwcFi9ejMVi4Z577uGFF14w1WFsbCyJiYmMGzeO4OBg+vXrx4MPPshPfvITZsyYwYoVK+jXrx/p6ekATJgwgVmzZuF0OomMjGT58uXAxTsLjxo1itGjRxMUFER2drb3WSciIuIfrQqRl156iSVLlhAZGQlAbW0tS5YsMX3W1vTp05k+fXqztpiYGO9pvf8oLCyMl1566bKfM3nyZCZPnmyqBhER+ee1anfW119/7Q0QuLh76+DBgz4rSkRE2odWhciFCxeoq6vz/l5bW6sL+0REpHW7sx599FEefPBBRo4cCcBHH33EU0895dPCRESk7WtViKSmpnLXXXexe/duAHJzc7njjjt8WpiIiLR9rb4U+I477lBwiIhIM1d9K3gREZFLFCIiImKaQkRERExTiIiIiGkKERERMU0hIiIipilERETENIWIiIiYphARERHTFCIiImKaQkRERExTiIiIiGkBCZH6+nqmT5/OyJEjGTVqFJ9//jm1tbVkZGSQkJBARkaG9/klhmGwYMECnE4nKSkpHDhwwPs5BQUFJCQkkJCQ4H0Ou4iI+E9AQmThwoXcf//9fPTRR2zcuJFevXqRl5fHkCFD2LJlC0OGDCEvLw+A4uJiKioq2LJlCzk5OcydOxe4+GCs3Nxc1qxZw9q1a8nNzW324CwREfE9v4fIqVOn+PTTT5kwYQIAoaGhRERE4HK5SE1NBS4+v2Tbtm0A3naLxcLAgQOpr6+nurqa0tJS4uLisFqtREZGEhcXR0lJib+HIyLSobX6eSLXytGjR+nWrRtZWVkcOnSI/v37M2fOHE6cOEF0dDQAUVFRnDhxAgC3243dbve+326343a7v9Nus9lwu93f239QkAWr9cZrPKrrQ1udl7Zal3w/fXet117nyu8h0tTUxFdffcVzzz1HbGwsCxYs8O66usRisWCxWHzSv8djUFv7rU8++1qJigoPSL9XmpdA1QRXrku+n7671tNctaylufH77iy73Y7dbic2NhaAkSNH8tVXX9G9e3eqq6sBqK6uplu3bsDFLYyqqirv+6uqqrDZbN9pd7vd2Gw2P45ERET8HiJRUVHY7Xb++7//G4Bdu3bRq1cvHA4HhYWFABQWFjJ8+HAAb7thGOzbt4/w8HCio6OJj4+ntLSUuro66urqKC0tJT4+3t/DERHp0Py+Owvgueee4ze/+Q2NjY3ExMSwePFiLly4QGZmJuvWraNHjx6sWLECgGHDhrFz506cTiedO3dm0aJFAFitVqZMmeI9QD916lSsVmsghiMi0mEFJET69evHhg0bvtP++uuvf6fNYrHw/PPPX/ZzJkyY4A0RERHxP12xLiIipilERETENIWIiIiYphARERHTFCIiImKaQkRERExTiIiIiGkBuU5ERERax2oNISTkBr/329h4jtraxu9dTiEiItKGhYTcwJq19/q93wfS9wDfHyLanSUiIqYpRERExDSFiIiImKYQERER0xQiIiJimkJERERMU4iIiIhpAQsRj8dDamoqv/zlLwGorKwkPT0dp9NJZmYmDQ0NADQ0NJCZmYnT6SQ9PZ2jR496P2PVqlU4nU4SExMpKSkJyDhERDqygIXIG2+8Qa9evby/L1u2jEmTJrF161YiIiJYt24dAGvXriUiIoKtW7cyadIkli1bBkB5eTlFRUUUFRWRn5/PvHnz8Hg8ARmLiEhHFZAQqaqqYseOHd5H2xqGwe7du0lMTARg3LhxuFwuALZv3864ceMASExMZNeuXRiGgcvlIikpidDQUGJiYujZsydlZWWBGI6ISIcVkBBZtGgRs2bNolOni93X1NQQERFBcPDFu7DY7XbcbjcAbrebW265BYDg4GDCw8OpqanB7XZjt9u9n2mz2bzvERER//D7vbM+/vhjunXrxl133cUnn3zi7+4JCrJgtd7o937bg7Y6L221Lvl++u5ary3OVWtq8nuI7N27l+3bt1NcXMz58+c5ffo0CxcupL6+nqamJoKDg6mqqsJmswEXtzCOHTuG3W6nqamJU6dO0bVrV2w2G1VVVd7Pdbvd3vdcicdjUFv7rc/Gdy1ERYUHpN8rzUugaoIr1yXfT99d67XFuWorNbVUh993Z/3617+muLiY7du38/vf/5777ruPF198kcGDB7N582YACgoKcDgcADgcDgoKCgDYvHkz9913HxaLBYfDQVFREQ0NDVRWVlJRUcGAAQP8PRwRkQ6tzVwnMmvWLF599VWcTie1tbWkp6cDMGHCBGpra3E6nbz66qv85je/AaB3796MGjWK0aNH8/jjj5OdnU1QUFAghyAi0uEE9HkigwcPZvDgwQDExMR4T+v9R2FhYbz00kuXff/kyZOZPHmyT2sUEZGWtZktERERaX8UIiIiYppCRERETFOIiIiIaQoRERExTSEiIiKmKURERMQ0hYiIiJimEBEREdMCesW6iHRcXSM7Exzq3/8FNTU0UVN31q99Xu8UIiISEMGhwRxcuN2vffab4/Brfx2BQkTara5dQgjufIPf+206e46a042XfS0yojOhYf7/s2o430Rdvf6FLf6nEJF2K7jzDewcOszv/Q4r3gkthEhoWDC5v37fzxXBr15M8XufIqAD6yIi8k9QiIiIiGkKERERMU0hIiIipvk9RI4dO8bDDz/M6NGjSUpK4vXXXwegtraWjIwMEhISyMjIoK6uDgDDMFiwYAFOp5OUlBQOHDjg/ayCggISEhJISEjwPoddRET8x+8hEhQUxLPPPssHH3zAn//8Z95++23Ky8vJy8tjyJAhbNmyhSFDhpCXlwdAcXExFRUVbNmyhZycHObOnQtcDJ3c3FzWrFnD2rVryc3N9QaPiIj4h99DJDo6mv79+wPQpUsXbr/9dtxuNy6Xi9TUVABSU1PZtm0bgLfdYrEwcOBA6uvrqa6uprS0lLi4OKxWK5GRkcTFxVFSUuLv4YiIdGgBvU7k6NGjHDx4kNjYWE6cOEF0dDQAUVFRnDhxAgC3243dbve+x26343a7v9Nus9lwu93f22dQkAWr9cZrPJLrQ1udl7ZYl2pqHdXUem2xrtbUFLAQOXPmDNOnT2f27Nl06dKl2WsWiwWLxeKTfj0eg9rabwHoEnEDncNCfNLPlZw938jp+nMtvh4VFe7Hav6/S/NyOYGqCVquSzU1155qAq3n/1tb//5aqiMgIdLY2Mj06dNJSUkhISEBgO7du1NdXU10dDTV1dV069YNuLiFUVVV5X1vVVUVNpsNm83Gnj17vO1ut5t77733quroHBbC/5n1xjUY0dX52+9+wWlaDhERkfbC78dEDMNgzpw53H777WRkZHjbHQ4HhYWFABQWFjJ8+PBm7YZhsG/fPsLDw4mOjiY+Pp7S0lLq6uqoq6ujtLSU+Ph4fw9HRKRD8/uWyN/+9jc2btzInXfeydixYwGYOXMmTz75JJmZmaxbt44ePXqwYsUKAIYNG8bOnTtxOp107tyZRYsWAWC1WpkyZQoTJkwAYOrUqVitVn8PR0SkQ/N7iPz4xz/m66+/vuxrl64Z+UcWi4Xnn3/+sstPmDDBGyIiIuJ/umJdRERMU4iIiIhpChERETFNISIiIqYpRERExDSFiIiImKYQERER0xQiIiJimkJERERMU4iIiIhpChERETFNISIiIqYpRERExDSFiIiImKYQERER0xQiIiJimkJERERMa/chUlxcTGJiIk6nk7y8vECXIyLSobTrEPF4PMyfP5/8/HyKiorYtGkT5eXlgS5LRKTDaNchUlZWRs+ePYmJiSE0NJSkpCRcLlegyxIR6TAshmEYgS7CrI8++oiSkhIWLlwIQGFhIWVlZWRnZwe4MhGRjqFdb4mIiEhgtesQsdlsVFVVeX93u93YbLYAViQi0rG06xC5++67qaiooLKykoaGBoqKinA4HIEuS0SkwwgOdAH/jODgYLKzs3n88cfxeDyMHz+e3r17B7osEZEOo10fWBcRkcBq17uzREQksBQiIiJimkJERERM65Ahcvz4cWbMmMGIESNIS0vjiSee4PDhwyQnJ/u03w8//JCkpCT69u3Ll19+2SZqWrJkCSNHjiQlJYWpU6dSX18f8JpWrFhBSkoKY8eO5dFHH8Xtdjd7PVB1XbJ69Wr69OnDyZMnA17Tyy+/zP3338/YsWMZO3YsO3fuDHhNAG+++SYjR44kKSmJpUuXBrymzMxM7xw5HA7Gjh0b8JoOHjzIAw88wNixY0lLS6OsrKzZ64Gq69ChQzz44IOkpKTw1FNPcfr06Su/wehgLly4YDzwwAPG22+/7W07ePCg8emnnxpJSUk+7bu8vNz4r//6L2PixIlGWVlZm6ippKTEaGxsNAzDMJYuXWosXbo04DWdOnXK+/Prr79uPPfcc97fA1mXYRjG//zP/xiPPvqo8ZOf/MQ4ceJEwGt66aWXjPz8/O+0B7KmXbt2GY888ohx/vx5wzAM4+9//3vAa/pHixcvNl5++eWA15SRkWHs2LHDMAzD2LFjhzFx4kTva4GsKy0tzfjkk08MwzCMtWvXGsuXL7/i8h1uS2T37t0EBwfzs5/9zNvWt29f7Ha79/ejR4/y0EMPMW7cOMaNG8fevXsBqK6u5uc//zljx44lOTmZzz77DI/Hw7PPPktycjIpKSm89tprLfbdq1cvbr/99jZVU3x8PMHBF8/0HjhwoPfizUDW1KVLF+/PZ8+exWKxtIm5Ali8eDGzZs1qUzVdTiBreuedd3jyyScJDQ0FoHv37gGv6RLDMPjwww+9/5oPZE0Wi4UzZ84AcOrUKaKjo72vBbKuiooKBg0aBEBcXBxbtmy54py26+tEzPjP//xP+vfvf8VlunfvzquvvkpYWBgVFRXMnDmTDRs2sGnTJuLj45k8eTIej4ezZ89y8OBB3G43mzZtAmi2O6i91bR+/XpGjRrVJmpavnw5hYWFhIeH88Ybb3jbA1nXtm3biI6Opm/fvs3aAz1Xf/rTnygsLOSuu+7i2WefJTIyMqA1VVRU8Nlnn7F8+XLCwsJ45plnGDBgQMDnCeCzzz6je/fu/PCHPwQC+93Nnj2bxx57jCVLlnDhwgXeffdd72uBrKt37964XC5GjBjBRx99xLFjx65YR4cLkdZoampi/vz5HDp0iE6dOlFRUQFcvEJ+9uzZNDU1MWLECPr160dMTAyVlZXk5OQwbNgw4uPj22VNK1euJCgoiDFjxrSJmmbMmMGMGTNYtWoVb731FtOnTw9oXWfPnmXVqlWsXr261XX4uiaAn/3sZ0yZMgWLxcIf/vAHXnjhBRYvXhzQmjweD3V1daxZs4Yvv/ySzMzMVt9d29fr+aZNm676mIKvanrnnXfIysoiMTGRDz74gDlz5lzVVqev6lq4cCELFy7kj3/8Iw6Hw7tF2ZIOtzurd+/eHDhw4IrLvPbaa9x8881s3LiR9evX09jYCMCgQYN46623sNlsPPvssxQWFhIZGcnGjRu59957effdd5kzZ067q2nDhg3s2LGDZcuWeXfTBLqmS1JSUpptTgeqriNHjnD06FHvgdmqqirS0tI4fvx4QOfq5ptvJigoiE6dOpGenu49YSOQNdlsNpxOJxaLhQEDBtCpUydqamoCvk41NTWxdetWRo8e7W0LZE0FBQUkJCQAMGrUqGYH1gNZV69evVi9ejUbNmwgKSmJmJiYK9bR4ULkvvvuo6GhgT//+c/etkOHDjW7keOpU6eIioqiU6dObNy4EY/HA8A333zDzTffzAMPPEB6ejoHDhzg5MmTGIZBYmIimZmZfPXVV+2qpuLiYvLz81m5ciWdO3duEzVd+hcVgMvlanYcKVB19enTh127drF9+3a2b9+O3W5nw4YNREVFBXSuqqurvT9v27bNe9ufQNY0YsQIPvnkEwAOHz5MY2MjXbt2Dfjf3l//+lduv/32ZscVAllTdHQ0e/bsAS4eA7m0iy3QdZ04cQKACxcusHLlSv71X//1ivPa4XZnWSwWcnNzWbRoEf/xH/9BWFgYt956K7Nnz/Yu89BDDzFt2jQKCwu5//77ufHGGwHYs2cPr7zyCsHBwdx4440sWbKE6upqsrKyuHDhAgAzZ85sse+tW7eSk5PDyZMn+eUvf0m/fv145ZVXAlpTTk4ODQ0NZGRkABAbG8v8+fMDWtOLL77I4cOHsVgs3HrrrcybN69NfH8tCWRNv/vd7zh06BAAt956K/Pnzw94TePHj2f27NkkJycTEhLCCy+84N3CDeR398EHH5CUlNSsLdB/e4sWLaKpqYmwsDDvdxfoujZt2sTbb78NgNPpZPz48VecV907S0RETOtwu7NEROTa6XC7s/xh3rx53nO2L/nFL37xvZuFvqSaWq8t1qWaWkc1td61qku7s0RExDTtzhIREdMUIiIiYppCROQaqq+v509/+pPP+9m2bRvl5eU+70fk+yhERK6h+vp63nnnnVYvbxiG99z9q6EQkbZCB9ZFrqEZM2bgcrn4l3/5FwYPHszXX39NfX09TU1NPP3004wYMYKjR4/y2GOPERsby4EDB8jLy6OwsJD33nuPbt26ccstt9C/f38ee+wxjhw5wrx586ipqeGGG24gJyeHuro6nnrqKbp06UJ4eDgvv/wyP/jBDwI9dOmorv3d6EU6rsrKSu/zHhobG73PRjlx4oQxYsQI48KFC0ZlZaXRp08f4/PPPzcMwzC++OILY8yYMca5c+eMU6dOGU6n0/uMkF/84hfG4cOHDcMwjH379hkPP/ywYRiG8dvf/tb48MMP/Ts4kcvQdSIiPmIYBr///e/59NNP6dSpE263m7///e8A9OjRg4EDBwKwd+9ehg8fTlhYGGFhYfz0pz8F4MyZM3z++ec8/fTT3s9saGjw+zhErkQhIuIj77//PidPnmTDhg2EhITgcDg4f/48gPc+R1diGAYRERFs3LjR16WKmKYD6yLX0E033dTsaXXdu3cnJCSE3bt3880331z2PT/60Y/4+OOPOX/+PGfOnGHHjh3AxSc83nbbbXz44YfAxVC5dLPFf+xHJJAUIiLXUNeuXfnRj35EcnIyhw4dYv/+/aSkpLBx48bLPhoZYMCAATgcDsaMGcMTTzzBnXfeSXh4OHDxLr3r1q1jzJgxJCUlsW3bNgBGjx7NK6+8QmpqKkeOHPHb+ET+N52dJdIGnDlzhptuuomzZ8/y85//nJycnO99PKpIW6BjIiJtQHZ2NuXl5Zw/f55x48YpQKTd0JaIiIiYpmMiIiJimkJERERMU4iIiIhpChERETFNISIiIqb9Xz63Bobt0NmeAAAAAElFTkSuQmCC\n"},"metadata":{}}]},{"cell_type":"markdown","source":"The following 3 code cells are due to [@nagamiso](https://www.kaggle.com/nagomiso/feature-extraction-tfidf).","metadata":{}},{"cell_type":"code","source":"class_to_order = dict()\norder_to_class = dict()\n\nfor idx, col in enumerate(train.target.unique()):\n    order_to_class[idx] = col\n    class_to_order[col] = idx\n\ntrain[\"target_ord\"] = train[\"target\"].map(class_to_order).astype(\"int16\")\nfeature_columns = [col for col in train.columns if col.startswith(\"feat_\")]\ntarget_column = [\"target_ord\"]","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:44:00.492031Z","iopub.execute_input":"2021-06-08T12:44:00.492387Z","iopub.status.idle":"2021-06-08T12:44:00.510597Z","shell.execute_reply.started":"2021-06-08T12:44:00.492347Z","shell.execute_reply":"2021-06-08T12:44:00.509832Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"order_to_class","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:44:00.512129Z","iopub.execute_input":"2021-06-08T12:44:00.512466Z","iopub.status.idle":"2021-06-08T12:44:00.520872Z","shell.execute_reply.started":"2021-06-08T12:44:00.512430Z","shell.execute_reply":"2021-06-08T12:44:00.520080Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"{0: 'Class_1',\n 1: 'Class_2',\n 2: 'Class_3',\n 3: 'Class_4',\n 4: 'Class_5',\n 5: 'Class_6',\n 6: 'Class_7',\n 7: 'Class_8',\n 8: 'Class_9'}"},"metadata":{}}]},{"cell_type":"code","source":"class_to_order","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:44:00.522156Z","iopub.execute_input":"2021-06-08T12:44:00.522507Z","iopub.status.idle":"2021-06-08T12:44:00.528258Z","shell.execute_reply.started":"2021-06-08T12:44:00.522470Z","shell.execute_reply":"2021-06-08T12:44:00.527319Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"{'Class_1': 0,\n 'Class_2': 1,\n 'Class_3': 2,\n 'Class_4': 3,\n 'Class_5': 4,\n 'Class_6': 5,\n 'Class_7': 6,\n 'Class_8': 7,\n 'Class_9': 8}"},"metadata":{}}]},{"cell_type":"markdown","source":"We are now going to see how each of the features are skewed. This would help us in further analysis.","metadata":{}},{"cell_type":"code","source":"from scipy.stats import skew","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:44:00.531907Z","iopub.execute_input":"2021-06-08T12:44:00.532265Z","iopub.status.idle":"2021-06-08T12:44:00.536860Z","shell.execute_reply.started":"2021-06-08T12:44:00.532230Z","shell.execute_reply":"2021-06-08T12:44:00.535617Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"skew = []\nfor i in train[feature_columns].columns:\n    skew.append(train[str(i)].skew())\n    \nskew_df = pd.DataFrame({'Feature': train[feature_columns].columns, 'Skewness': skew})\nskew_df.plot(kind='bar',figsize=(18,10))","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:44:00.539039Z","iopub.execute_input":"2021-06-08T12:44:00.539383Z","iopub.status.idle":"2021-06-08T12:44:02.478332Z","shell.execute_reply.started":"2021-06-08T12:44:00.539345Z","shell.execute_reply":"2021-06-08T12:44:02.477462Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1296x720 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"We are now going to apply the [QuantileTransformer](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.QuantileTransformer.html#sklearn.preprocessing.QuantileTransformer) from scikit-learn. I first used `StandardScaler` but found that there was no change in the skew value of the features. \n\nIf anyone knows why I didn't see any change, please drop a comment!\n***","metadata":{}},{"cell_type":"markdown","source":"From the scikit-learn website:\n> QuantileTransformer applies a non-linear transformation such that the probability density function of each feature will be mapped to a uniform or Gaussian distribution. In this case, all the data, including outliers, will be mapped to a uniform distribution with the range $[0, 1]$, making outliers indistinguishable from inliers.\n\n> RobustScaler and QuantileTransformer are robust to outliers in the sense that adding or removing outliers in the training set will yield approximately the same transformation. But contrary to RobustScaler, QuantileTransformer will also automatically collapse any outlier by setting them to the a priori defined range boundaries (0 and 1). This can result in saturation artifacts for extreme values.\n\n> To map to a Gaussian distribution, set the parameter `output_distribution='normal'`.","metadata":{}},{"cell_type":"code","source":"from sklearn.preprocessing import QuantileTransformer\ntrain[feature_columns] = QuantileTransformer(copy=False, output_distribution='normal').fit_transform(train[feature_columns])\ntest[feature_columns] = QuantileTransformer(copy=False, output_distribution='normal').fit_transform(test[feature_columns])","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:44:02.479668Z","iopub.execute_input":"2021-06-08T12:44:02.480164Z","iopub.status.idle":"2021-06-08T12:44:11.810773Z","shell.execute_reply.started":"2021-06-08T12:44:02.480121Z","shell.execute_reply":"2021-06-08T12:44:11.809879Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"markdown","source":"Let us now check the skew values of the features.","metadata":{}},{"cell_type":"code","source":"skew = []\nfor i in train[feature_columns].columns:\n    skew.append(train[str(i)].skew())\n    \nskew_df = pd.DataFrame({'Feature': train[feature_columns].columns, 'Skewness': skew})\nskew_df.plot(kind='bar',figsize=(18,10))","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:44:11.812166Z","iopub.execute_input":"2021-06-08T12:44:11.812503Z","iopub.status.idle":"2021-06-08T12:44:13.792582Z","shell.execute_reply.started":"2021-06-08T12:44:11.812466Z","shell.execute_reply":"2021-06-08T12:44:13.791676Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1296x720 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"We are now going to remove the features that have a skew value > 3.75 (my arbitrary choice).","metadata":{}},{"cell_type":"code","source":"# check features for skew\nskew_feats = train[feature_columns].skew().sort_values(ascending=False)\nskewness = pd.DataFrame({'Skew': skew_feats})\nskewness = skewness[abs(skewness) > 3.75].dropna()\nskewed_features = skewness.index.values.tolist()\nskewed_features","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:44:13.793946Z","iopub.execute_input":"2021-06-08T12:44:13.794319Z","iopub.status.idle":"2021-06-08T12:44:13.945669Z","shell.execute_reply.started":"2021-06-08T12:44:13.794278Z","shell.execute_reply":"2021-06-08T12:44:13.944830Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"['feat_6', 'feat_84', 'feat_51', 'feat_5', 'feat_81', 'feat_77']"},"metadata":{}}]},{"cell_type":"code","source":"train_new = train.drop(skewed_features, axis = 1)\ntrain_new","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:44:13.947007Z","iopub.execute_input":"2021-06-08T12:44:13.947538Z","iopub.status.idle":"2021-06-08T12:44:14.040314Z","shell.execute_reply.started":"2021-06-08T12:44:13.947499Z","shell.execute_reply":"2021-06-08T12:44:14.039462Z"},"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"          id    feat_1    feat_2    feat_3    feat_4    feat_7    feat_8  \\\n0          1  1.174387 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n1          2 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338  0.869846   \n2          3 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338  0.869846   \n3          4  1.174387 -5.199338 -5.199338  0.959440  2.432017 -5.199338   \n4          5 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n...      ...       ...       ...       ...       ...       ...       ...   \n61873  61874  1.174387 -5.199338 -5.199338  0.959440 -5.199338 -5.199338   \n61874  61875  1.994971 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n61875  61876 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338  1.639539   \n61876  61877  1.174387 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n61877  61878 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n\n         feat_9   feat_10   feat_11   feat_12   feat_13   feat_14   feat_15  \\\n0     -5.199338 -5.199338  0.779924 -5.199338 -5.199338 -5.199338 -5.199338   \n1     -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n2     -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n3     -5.199338  1.355761  0.779924 -5.199338  1.128144 -5.199338 -5.199338   \n4     -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n...         ...       ...       ...       ...       ...       ...       ...   \n61873 -5.199338 -5.199338 -5.199338  1.529558  1.824556 -5.199338 -5.199338   \n61874 -5.199338 -5.199338 -5.199338 -5.199338  1.951050  0.683178 -5.199338   \n61875  1.033647 -5.199338 -5.199338 -5.199338 -5.199338  0.349483 -5.199338   \n61876 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n61877 -5.199338 -5.199338 -5.199338 -5.199338  1.824556 -5.199338  0.746351   \n\n        feat_16   feat_17   feat_18   feat_19   feat_20   feat_21   feat_22  \\\n0     -5.199338  1.644369 -5.199338 -5.199338 -5.199338 -5.199338  0.651824   \n1     -5.199338 -5.199338  1.310017 -5.199338 -5.199338 -5.199338 -5.199338   \n2     -5.199338  1.202347 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n3      0.253606  1.202347 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n4     -5.199338  2.074445 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n...         ...       ...       ...       ...       ...       ...       ...   \n61873  2.144011 -5.199338  1.310017 -5.199338 -5.199338 -5.199338  2.386340   \n61874 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n61875 -5.199338 -5.199338  0.814401 -5.199338 -5.199338 -5.199338 -5.199338   \n61876  0.955471 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n61877  0.253606  1.202347  0.814401 -5.199338 -5.199338 -5.199338  1.471555   \n\n        feat_23   feat_24   feat_25   feat_26   feat_27   feat_28   feat_29  \\\n0     -5.199338  0.833781  0.152391  1.004131 -5.199338 -5.199338  1.934489   \n1     -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n2     -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n3     -5.199338  1.295363  0.636387  1.340200 -5.199338 -5.199338 -5.199338   \n4     -5.199338 -0.118204 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n...         ...       ...       ...       ...       ...       ...       ...   \n61873 -5.199338  0.610484  1.602210  1.004131 -5.199338 -5.199338  3.718764   \n61874 -5.199338 -5.199338  0.636387 -5.199338 -5.199338 -5.199338  1.425011   \n61875 -5.199338 -0.118204 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n61876 -5.199338  0.303168  0.152391 -5.199338 -5.199338  1.337127  1.934489   \n61877 -5.199338  0.303168  0.152391 -5.199338 -5.199338 -5.199338  2.635514   \n\n        feat_30   feat_31   feat_32   feat_33   feat_34   feat_35   feat_36  \\\n0     -5.199338 -5.199338 -5.199338 -5.199338 -5.199338  0.983573 -5.199338   \n1     -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n2     -5.199338 -5.199338 -5.199338  0.644086 -5.199338 -5.199338 -5.199338   \n3      3.365547 -5.199338  2.325972 -5.199338 -5.199338 -5.199338 -5.199338   \n4     -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n...         ...       ...       ...       ...       ...       ...       ...   \n61873  1.664082 -5.199338  1.337127  1.554270  0.912774  0.983573  0.893908   \n61874 -5.199338 -5.199338 -5.199338  1.171890  0.912774 -5.199338  1.327982   \n61875 -5.199338 -5.199338 -5.199338  1.554270 -5.199338 -5.199338  1.327982   \n61876 -5.199338 -5.199338  0.965420  0.644086 -5.199338 -5.199338  1.902873   \n61877 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338  1.327982   \n\n        feat_37   feat_38   feat_39   feat_40   feat_41   feat_42   feat_43  \\\n0     -5.199338 -5.199338 -5.199338  0.304482 -5.199338  1.865852 -5.199338   \n1      1.223287 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n2     -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n3     -5.199338  1.634747 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n4     -5.199338 -5.199338 -5.199338 -5.199338  1.192071 -5.199338 -5.199338   \n...         ...       ...       ...       ...       ...       ...       ...   \n61873  1.780005  0.884593 -5.199338  0.703922  1.786156  1.286702 -5.199338   \n61874 -5.199338 -5.199338 -5.199338 -5.199338  1.192071 -5.199338 -5.199338   \n61875 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n61876 -5.199338 -5.199338 -5.199338 -5.199338  1.192071  1.286702 -5.199338   \n61877 -5.199338 -5.199338 -5.199338 -5.199338  2.144011  0.824933 -5.199338   \n\n        feat_44   feat_45   feat_46   feat_47   feat_48   feat_49   feat_50  \\\n0     -5.199338 -5.199338 -5.199338 -5.199338  0.587964 -5.199338 -5.199338   \n1     -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n2     -5.199338 -5.199338 -5.199338 -5.199338  0.147316 -5.199338 -5.199338   \n3      1.310017 -5.199338  1.404533 -5.199338  0.147316  1.664082  1.307063   \n4     -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338  1.721914   \n...         ...       ...       ...       ...       ...       ...       ...   \n61873  1.926403 -5.199338 -5.199338 -5.199338  1.951050 -5.199338 -5.199338   \n61874 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n61875 -5.199338 -5.199338 -5.199338 -5.199338  0.905189 -5.199338 -5.199338   \n61876  0.842694 -5.199338 -5.199338 -5.199338  0.147316 -5.199338 -5.199338   \n61877 -5.199338 -5.199338 -5.199338 -5.199338  0.147316 -5.199338 -5.199338   \n\n        feat_52   feat_53   feat_54   feat_55   feat_56   feat_57   feat_58  \\\n0     -5.199338 -5.199338  0.287442 -5.199338 -5.199338  1.649237 -5.199338   \n1     -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338  1.266860   \n2     -5.199338 -5.199338 -5.199338  1.157061 -5.199338 -5.199338 -5.199338   \n3     -5.199338 -5.199338  0.803958  1.157061 -5.199338 -5.199338 -5.199338   \n4     -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n...         ...       ...       ...       ...       ...       ...       ...   \n61873 -5.199338  2.456904  1.340200  2.023292  1.421556 -5.199338 -5.199338   \n61874 -5.199338  1.700222 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n61875 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n61876 -5.199338  1.700222  0.287442 -5.199338 -5.199338  1.244778 -5.199338   \n61877 -5.199338  1.700222  1.104747  1.157061 -5.199338  1.244778 -5.199338   \n\n        feat_59   feat_60   feat_61   feat_62   feat_63   feat_64   feat_65  \\\n0     -5.199338  1.780005 -5.199338  0.434861  1.541796 -5.199338  1.312981   \n1     -5.199338 -5.199338 -5.199338 -5.199338 -5.199338  0.642543 -5.199338   \n2     -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n3     -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338  1.926403   \n4     -5.199338 -5.199338 -5.199338  0.434861 -5.199338 -5.199338 -5.199338   \n...         ...       ...       ...       ...       ...       ...       ...   \n61873 -5.199338  0.897655 -5.199338  2.947533 -5.199338  0.642543  2.611712   \n61874 -5.199338 -5.199338 -5.199338  0.434861  2.096521 -5.199338 -5.199338   \n61875  2.597412 -5.199338 -5.199338  1.435477 -5.199338 -5.199338 -5.199338   \n61876 -5.199338 -5.199338 -5.199338  0.882739 -5.199338 -5.199338 -5.199338   \n61877 -5.199338  1.387908 -5.199338  1.435477 -5.199338 -5.199338 -5.199338   \n\n        feat_66   feat_67   feat_68   feat_69   feat_70   feat_71   feat_72  \\\n0     -5.199338  1.199766 -5.199338 -5.199338 -5.199338  1.207532 -5.199338   \n1     -5.199338 -0.084155 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n2     -5.199338  1.046576 -5.199338 -5.199338  1.027247 -5.199338 -5.199338   \n3      0.819656  0.888309 -5.199338 -5.199338  1.562721 -5.199338 -5.199338   \n4     -5.199338 -5.199338  1.275302 -5.199338 -5.199338  2.004234 -5.199338   \n...         ...       ...       ...       ...       ...       ...       ...   \n61873  2.877846  0.496390 -5.199338 -5.199338  1.324958 -5.199338  0.800496   \n61874  0.819656  0.888309 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n61875 -5.199338  2.063775 -5.199338 -5.199338 -5.199338 -5.199338  1.228607   \n61876 -5.199338  1.046576 -5.199338 -5.199338 -5.199338  1.207532 -5.199338   \n61877 -5.199338  1.566990  1.644369 -5.199338 -5.199338 -5.199338 -5.199338   \n\n        feat_73   feat_74   feat_75   feat_76   feat_78   feat_79   feat_80  \\\n0     -5.199338 -5.199338 -5.199338 -5.199338 -5.199338  1.721914  0.969427   \n1      1.615945  1.072973 -5.199338  0.997920  1.467860 -5.199338 -5.199338   \n2     -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n3      1.615945  1.072973 -5.199338  0.997920 -5.199338  1.365257  0.969427   \n4     -5.199338 -5.199338 -5.199338 -5.199338 -5.199338  2.096521 -5.199338   \n...         ...       ...       ...       ...       ...       ...       ...   \n61873  1.166919  1.475269 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n61874 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338  2.575483 -5.199338   \n61875 -5.199338 -5.199338 -5.199338  1.381364 -5.199338 -5.199338 -5.199338   \n61876 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338 -5.199338   \n61877 -5.199338  1.773922  1.037937 -5.199338 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      feat_90   feat_91   feat_92   feat_93   target  target_ord  \n0     -5.199338 -5.199338 -5.199338 -5.199338  Class_1           0  \n1     -5.199338 -5.199338 -5.199338 -5.199338  Class_1           0  \n2     -5.199338 -5.199338 -5.199338 -5.199338  Class_1           0  \n3     -5.199338 -5.199338 -5.199338 -5.199338  Class_1           0  \n4      1.255743 -5.199338 -5.199338 -5.199338  Class_1           0  \n...         ...       ...       ...       ...      ...         ...  \n61873 -5.199338 -5.199338  1.575613 -5.199338  Class_9           8  \n61874 -5.199338 -5.199338  1.035790 -5.199338  Class_9           8  \n61875 -5.199338 -5.199338 -5.199338 -5.199338  Class_9           8  \n61876 -5.199338  2.169694  3.121095 -5.199338  Class_9           8  \n61877 -5.199338 -5.199338  1.575613 -5.199338  Class_9           8  \n\n[61878 rows x 90 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>feat_1</th>\n      <th>feat_2</th>\n      <th>feat_3</th>\n      <th>feat_4</th>\n      <th>feat_7</th>\n      <th>feat_8</th>\n      <th>feat_9</th>\n      <th>feat_10</th>\n      <th>feat_11</th>\n      <th>feat_12</th>\n      <th>feat_13</th>\n      <th>feat_14</th>\n      <th>feat_15</th>\n      <th>feat_16</th>\n      <th>feat_17</th>\n      <th>feat_18</th>\n      <th>feat_19</th>\n      <th>feat_20</th>\n      <th>feat_21</th>\n      <th>feat_22</th>\n      <th>feat_23</th>\n      <th>feat_24</th>\n      <th>feat_25</th>\n      <th>feat_26</th>\n      <th>feat_27</th>\n      <th>feat_28</th>\n      <th>feat_29</th>\n      <th>feat_30</th>\n      <th>feat_31</th>\n      <th>feat_32</th>\n      <th>feat_33</th>\n      <th>feat_34</th>\n      <th>feat_35</th>\n      <th>feat_36</th>\n      <th>feat_37</th>\n      <th>feat_38</th>\n      <th>feat_39</th>\n      <th>feat_40</th>\n      <th>feat_41</th>\n      <th>feat_42</th>\n      <th>feat_43</th>\n      <th>feat_44</th>\n      <th>feat_45</th>\n      <th>feat_46</th>\n      <th>feat_47</th>\n      <th>feat_48</th>\n      <th>feat_49</th>\n      <th>feat_50</th>\n      <th>feat_52</th>\n      <th>feat_53</th>\n      <th>feat_54</th>\n      <th>feat_55</th>\n      <th>feat_56</th>\n      <th>feat_57</th>\n      <th>feat_58</th>\n      <th>feat_59</th>\n      <th>feat_60</th>\n      <th>feat_61</th>\n      <th>feat_62</th>\n      <th>feat_63</th>\n      <th>feat_64</th>\n      <th>feat_65</th>\n      <th>feat_66</th>\n      <th>feat_67</th>\n      <th>feat_68</th>\n      <th>feat_69</th>\n      <th>feat_70</th>\n      <th>feat_71</th>\n      <th>feat_72</th>\n      <th>feat_73</th>\n      <th>feat_74</th>\n      <th>feat_75</th>\n      <th>feat_76</th>\n      <th>feat_78</th>\n      <th>feat_79</th>\n      <th>feat_80</th>\n      <th>feat_82</th>\n      <th>feat_83</th>\n      <th>feat_85</th>\n      <th>feat_86</th>\n      <th>feat_87</th>\n      <th>feat_88</th>\n      <th>feat_89</th>\n      <th>feat_90</th>\n      <th>feat_91</th>\n      <th>feat_92</th>\n      <th>feat_93</th>\n      <th>target</th>\n      <th>target_ord</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1.174387</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>0.779924</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.644369</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>0.651824</td>\n      <td>-5.199338</td>\n      <td>0.833781</td>\n      <td>0.152391</td>\n      <td>1.004131</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.934489</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>0.983573</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>0.304482</td>\n      <td>-5.199338</td>\n      <td>1.865852</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>0.587964</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>0.287442</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.649237</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.780005</td>\n      <td>-5.199338</td>\n      <td>0.434861</td>\n      <td>1.541796</td>\n      <td>-5.199338</td>\n      <td>1.312981</td>\n      <td>-5.199338</td>\n      <td>1.199766</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.207532</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.721914</td>\n      <td>0.969427</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.027247</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>Class_1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>0.869846</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.310017</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.223287</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.266860</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>0.642543</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-0.084155</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.615945</td>\n      <td>1.072973</td>\n      <td>-5.199338</td>\n      <td>0.997920</td>\n      <td>1.467860</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>Class_1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>0.869846</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.202347</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>0.644086</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>0.147316</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.157061</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.046576</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.027247</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>Class_1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>1.174387</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>0.959440</td>\n      <td>2.432017</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.355761</td>\n      <td>0.779924</td>\n      <td>-5.199338</td>\n      <td>1.128144</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>0.253606</td>\n      <td>1.202347</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.295363</td>\n      <td>0.636387</td>\n      <td>1.340200</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>3.365547</td>\n      <td>-5.199338</td>\n      <td>2.325972</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.634747</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.310017</td>\n      <td>-5.199338</td>\n      <td>1.404533</td>\n      <td>-5.199338</td>\n      <td>0.147316</td>\n      <td>1.664082</td>\n      <td>1.307063</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>0.803958</td>\n      <td>1.157061</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.926403</td>\n      <td>0.819656</td>\n      <td>0.888309</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.562721</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.615945</td>\n      <td>1.072973</td>\n      <td>-5.199338</td>\n      <td>0.997920</td>\n      <td>-5.199338</td>\n      <td>1.365257</td>\n      <td>0.969427</td>\n      <td>1.851734</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>0.485069</td>\n      <td>1.644369</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>Class_1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>2.074445</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-0.118204</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.192071</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.721914</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>0.434861</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.275302</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>2.004234</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>2.096521</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.027247</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.255743</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>Class_1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>61873</th>\n      <td>61874</td>\n      <td>1.174387</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>0.959440</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.529558</td>\n      <td>1.824556</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>2.144011</td>\n      <td>-5.199338</td>\n      <td>1.310017</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>2.386340</td>\n      <td>-5.199338</td>\n      <td>0.610484</td>\n      <td>1.602210</td>\n      <td>1.004131</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>3.718764</td>\n      <td>1.664082</td>\n      <td>-5.199338</td>\n      <td>1.337127</td>\n      <td>1.554270</td>\n      <td>0.912774</td>\n      <td>0.983573</td>\n      <td>0.893908</td>\n      <td>1.780005</td>\n      <td>0.884593</td>\n      <td>-5.199338</td>\n      <td>0.703922</td>\n      <td>1.786156</td>\n      <td>1.286702</td>\n      <td>-5.199338</td>\n      <td>1.926403</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.951050</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>2.456904</td>\n      <td>1.340200</td>\n      <td>2.023292</td>\n      <td>1.421556</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>0.897655</td>\n      <td>-5.199338</td>\n      <td>2.947533</td>\n      <td>-5.199338</td>\n      <td>0.642543</td>\n      <td>2.611712</td>\n      <td>2.877846</td>\n      <td>0.496390</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.324958</td>\n      <td>-5.199338</td>\n      <td>0.800496</td>\n      <td>1.166919</td>\n      <td>1.475269</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.027247</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.575613</td>\n      <td>-5.199338</td>\n      <td>Class_9</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>61874</th>\n      <td>61875</td>\n      <td>1.994971</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.951050</td>\n      <td>0.683178</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>0.636387</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.425011</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.171890</td>\n      <td>0.912774</td>\n      <td>-5.199338</td>\n      <td>1.327982</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.192071</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.700222</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>0.434861</td>\n      <td>2.096521</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>0.819656</td>\n      <td>0.888309</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>2.575483</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>0.953493</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.497997</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.035790</td>\n      <td>-5.199338</td>\n      <td>Class_9</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>61875</th>\n      <td>61876</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.639539</td>\n      <td>1.033647</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>0.349483</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>0.814401</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-0.118204</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.554270</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.327982</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>0.905189</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>2.597412</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.435477</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>2.063775</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.228607</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.381364</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.275302</td>\n      <td>1.121060</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>Class_9</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>61876</th>\n      <td>61877</td>\n      <td>1.174387</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>0.955471</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>0.303168</td>\n      <td>0.152391</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.337127</td>\n      <td>1.934489</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>0.965420</td>\n      <td>0.644086</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.902873</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.192071</td>\n      <td>1.286702</td>\n      <td>-5.199338</td>\n      <td>0.842694</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>0.147316</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.700222</td>\n      <td>0.287442</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.244778</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>0.882739</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.046576</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.207532</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.014571</td>\n      <td>-5.199338</td>\n      <td>2.169694</td>\n      <td>3.121095</td>\n      <td>-5.199338</td>\n      <td>Class_9</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>61877</th>\n      <td>61878</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.824556</td>\n      <td>-5.199338</td>\n      <td>0.746351</td>\n      <td>0.253606</td>\n      <td>1.202347</td>\n      <td>0.814401</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.471555</td>\n      <td>-5.199338</td>\n      <td>0.303168</td>\n      <td>0.152391</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>2.635514</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.327982</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>2.144011</td>\n      <td>0.824933</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>0.147316</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.700222</td>\n      <td>1.104747</td>\n      <td>1.157061</td>\n      <td>-5.199338</td>\n      <td>1.244778</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.387908</td>\n      <td>-5.199338</td>\n      <td>1.435477</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.566990</td>\n      <td>1.644369</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.773922</td>\n      <td>1.037937</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.280981</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>-5.199338</td>\n      <td>1.575613</td>\n      <td>-5.199338</td>\n      <td>Class_9</td>\n      <td>8</td>\n    </tr>\n  </tbody>\n</table>\n<p>61878 rows × 90 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nfrom sklearn.metrics import log_loss\n\nX_train, X_valid, y_train, y_valid = train_test_split(\n    train_new.drop(['id', 'target', 'target_ord'], axis = 1), \n    train_new[target_column],\n    test_size = 0.275, \n    random_state = 7, \n    stratify = train_new[target_column]\n)","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:44:14.041657Z","iopub.execute_input":"2021-06-08T12:44:14.042034Z","iopub.status.idle":"2021-06-08T12:44:14.467107Z","shell.execute_reply.started":"2021-06-08T12:44:14.041995Z","shell.execute_reply":"2021-06-08T12:44:14.466107Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"markdown","source":"## Using KNN","metadata":{}},{"cell_type":"markdown","source":"From the scikit-learn documentation:\n\n> Neighbors-based classification is a type of instance-based learning or non-generalizing learning: it does not attempt to construct a general internal model, but simply stores instances of the training data. Classification is computed from a simple majority vote of the nearest neighbors of each point: a query point is assigned the data class which has the most representatives within the nearest neighbors of the point.\n\n> The $k$-neighbors classification in KNeighborsClassifier is the most commonly used technique. The optimal choice of $k$ is highly data-dependent: in general a larger $k$ suppresses the effects of noise, but makes the classification boundaries less distinct.\n\n> The basic nearest neighbors classification uses uniform weights: that is, the value assigned to a query point is computed from a simple majority vote of the nearest neighbors. Under some circumstances, it is better to weight the neighbors such that nearer neighbors contribute more to the fit. This can be accomplished through the weights keyword. The default value, `weights = uniform`, assigns uniform weights to each neighbor. `weights = distance` assigns weights proportional to the inverse of the distance from the query point. Alternatively, a user-defined function of the distance can be supplied to compute the weights.","metadata":{}},{"cell_type":"code","source":"from sklearn.neighbors import KNeighborsClassifier","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:44:14.468542Z","iopub.execute_input":"2021-06-08T12:44:14.468960Z","iopub.status.idle":"2021-06-08T12:44:14.605262Z","shell.execute_reply.started":"2021-06-08T12:44:14.468919Z","shell.execute_reply":"2021-06-08T12:44:14.604408Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"knc = KNeighborsClassifier(n_neighbors = 25, weights = 'distance')\nknc.fit(X_train, y_train)\nyhat = knc.predict(X_valid)","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:44:14.606554Z","iopub.execute_input":"2021-06-08T12:44:14.606935Z","iopub.status.idle":"2021-06-08T12:44:30.992070Z","shell.execute_reply.started":"2021-06-08T12:44:14.606898Z","shell.execute_reply":"2021-06-08T12:44:30.990879Z"},"trusted":true},"execution_count":16,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/sklearn/neighbors/_classification.py:179: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n  return self._fit(X, y)\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.metrics import classification_report, confusion_matrix","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:44:30.993349Z","iopub.execute_input":"2021-06-08T12:44:30.993712Z","iopub.status.idle":"2021-06-08T12:44:30.999554Z","shell.execute_reply.started":"2021-06-08T12:44:30.993654Z","shell.execute_reply":"2021-06-08T12:44:30.997621Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"markdown","source":"> A confusion matrix $C$ is such that $C_{i, j}$ is equal to the number of observations known to be in group $i$ and predicted to be in group $j$.","metadata":{}},{"cell_type":"code","source":"result = confusion_matrix(y_valid, yhat)\nprint(\"Confusion Matrix:\")\nprint(result)","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:44:31.000776Z","iopub.execute_input":"2021-06-08T12:44:31.001146Z","iopub.status.idle":"2021-06-08T12:44:31.039088Z","shell.execute_reply.started":"2021-06-08T12:44:31.001099Z","shell.execute_reply":"2021-06-08T12:44:31.038122Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stdout","text":"Confusion Matrix:\n[[ 120   50    5    0   45   44    3  103  160]\n [   2 3891  461   11   36    9   10    8    6]\n [   0 1234  928   11    3    6    7   10    2]\n [   1  452  129  123   10   14    5    2    4]\n [   0   29    3    0  719    1    0    0    1]\n [  11  124   12    5   48 3514   21   72   80]\n [  11  195   95    1   33   59  320   55   12]\n [  14  113    8    0   54  152    7 1913   67]\n [   8  129    7    0   39   47    5   45 1083]]\n","output_type":"stream"}]},{"cell_type":"markdown","source":"The `classification_report` function builds a text report showing the main classification metrics.\n\n$$\\text{Precision} = \\frac{\\text{# of True positives}}{\\text{# of True positives + # of False positives}}$$\n$$\\text{Recall} = \\frac{\\text{# of True positives}}{\\text{# of True positives + # of False negatives}}$$\n$$\\text{f1-score} = \\text{harmonic average of precision and recall} = \\frac{\\text{2 * Precision * Recall}}{\\text{Precision + Recall}}$$\n***\n$$ \\text{Accuracy} = \\frac{\\text{# of correct predictions}}{\\text{Total number of predictions}}$$\n$$\\text{Macro average - obtained by averaging the unweighted mean per label}$$\n$$\\text{Weighted average - obtained by averaging the support-weighted mean per label}$$\n***","metadata":{}},{"cell_type":"code","source":"result1 = classification_report(y_valid, yhat)\nprint(\"Classification Report:\")\nprint(result1)","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:44:31.040512Z","iopub.execute_input":"2021-06-08T12:44:31.040871Z","iopub.status.idle":"2021-06-08T12:44:31.074750Z","shell.execute_reply.started":"2021-06-08T12:44:31.040834Z","shell.execute_reply":"2021-06-08T12:44:31.073814Z"},"trusted":true},"execution_count":19,"outputs":[{"name":"stdout","text":"Classification Report:\n              precision    recall  f1-score   support\n\n           0       0.72      0.23      0.34       530\n           1       0.63      0.88      0.73      4434\n           2       0.56      0.42      0.48      2201\n           3       0.81      0.17      0.28       740\n           4       0.73      0.95      0.83       753\n           5       0.91      0.90      0.91      3887\n           6       0.85      0.41      0.55       781\n           7       0.87      0.82      0.84      2328\n           8       0.77      0.79      0.78      1363\n\n    accuracy                           0.74     17017\n   macro avg       0.76      0.62      0.64     17017\nweighted avg       0.75      0.74      0.72     17017\n\n","output_type":"stream"}]},{"cell_type":"code","source":"yhat_KNN = knc.predict_proba(X_valid)\nlogloss_KNN = log_loss(y_valid, yhat_KNN)\nprint('Log loss using KNN classifier:', logloss_KNN)","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:44:31.076113Z","iopub.execute_input":"2021-06-08T12:44:31.076454Z","iopub.status.idle":"2021-06-08T12:44:46.815103Z","shell.execute_reply.started":"2021-06-08T12:44:31.076418Z","shell.execute_reply":"2021-06-08T12:44:46.814198Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"Log loss using KNN classifier: 1.2113082274705085\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Using DecisionTree","metadata":{}},{"cell_type":"markdown","source":"From the scikit-learn documentation:\n> Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features. A tree can be seen as a piecewise constant approximation.","metadata":{}},{"cell_type":"code","source":"from sklearn.tree import DecisionTreeClassifier","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:44:46.816437Z","iopub.execute_input":"2021-06-08T12:44:46.816829Z","iopub.status.idle":"2021-06-08T12:44:46.850341Z","shell.execute_reply.started":"2021-06-08T12:44:46.816789Z","shell.execute_reply":"2021-06-08T12:44:46.849508Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"dtree = DecisionTreeClassifier(criterion='entropy', max_depth=8,\n                              min_samples_leaf = 6, max_leaf_nodes = 40,\n                              splitter = 'best')\ndtree.fit(X_train, y_train)\nyhat_tree = dtree.predict_proba(X_valid)\nlogloss_DTree = log_loss(y_valid, yhat_tree)\nprint('Log loss using Decision Tree: ', logloss_DTree)","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:44:46.851695Z","iopub.execute_input":"2021-06-08T12:44:46.852100Z","iopub.status.idle":"2021-06-08T12:44:47.170235Z","shell.execute_reply.started":"2021-06-08T12:44:46.852060Z","shell.execute_reply":"2021-06-08T12:44:47.169287Z"},"trusted":true},"execution_count":22,"outputs":[{"name":"stdout","text":"Log loss using Decision Tree:  1.1548008856900502\n","output_type":"stream"}]},{"cell_type":"code","source":"# Plot decision tree\nfrom IPython.display import Image as PImage\nfrom subprocess import check_call\nfrom PIL import Image, ImageDraw, ImageFont\nfrom sklearn import tree\n\ntree.plot_tree(dtree)","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:44:47.171467Z","iopub.execute_input":"2021-06-08T12:44:47.171991Z","iopub.status.idle":"2021-06-08T12:44:51.875298Z","shell.execute_reply.started":"2021-06-08T12:44:47.171950Z","shell.execute_reply":"2021-06-08T12:44:51.874468Z"},"trusted":true},"execution_count":23,"outputs":[{"execution_count":23,"output_type":"execute_result","data":{"text/plain":"[Text(280.4873161764706, 205.35999999999999, 'X[8] <= 1.218\\nentropy = 2.814\\nsamples = 44861\\nvalue = [1399, 11688, 5803, 1951, 1986, 10248, 2058, 6136, 3592]'),\n Text(236.0216911764706, 181.2, 'X[56] <= 1.421\\nentropy = 2.856\\nsamples = 39911\\nvalue = [1390, 11686, 5794, 1951, 1986, 5498, 2041, 5978, 3587]'),\n Text(186.47867647058825, 157.04, 'X[31] <= 1.643\\nentropy = 2.809\\nsamples = 36487\\nvalue = [1347, 11679, 5794, 1923, 1986, 2344, 1978, 5919, 3517]'),\n Text(131.7044117647059, 132.88, 'X[11] <= 0.786\\nentropy = 2.681\\nsamples = 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"Let us now export the tree in Graphviz format. Thanks to [@dmilla](https://www.kaggle.com/dmilla/introduction-to-decision-trees-titanic-dataset) for the code cell.","metadata":{}},{"cell_type":"code","source":"# Export our trained model as a .dot file\nwith open(\"otto.dot\", 'w') as f:\n     f = tree.export_graphviz(dtree, out_file=f, max_depth = 3, impurity = True, \n                              feature_names = train_new.drop(['id', 'target', 'target_ord'], axis = 1).columns.values.tolist(), \n                              class_names = train_new.target.unique().tolist(), \n                              rounded = True, filled = True)\n        \n#Convert .dot to .png to allow display in web notebook\ncheck_call(['dot','-Tpng','otto.dot','-o','otto.png'])\n\n# Annotating chart with PIL\nimg = Image.open(\"otto.png\")\ndraw = ImageDraw.Draw(img)\nimg.save('sample-out.png')\nPImage(\"sample-out.png\")","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:44:51.876671Z","iopub.execute_input":"2021-06-08T12:44:51.877020Z","iopub.status.idle":"2021-06-08T12:44:52.408626Z","shell.execute_reply.started":"2021-06-08T12:44:51.876981Z","shell.execute_reply":"2021-06-08T12:44:52.407811Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"image/png":"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\n","text/plain":"<IPython.core.display.Image object>"},"metadata":{}}]},{"cell_type":"markdown","source":"## Using Logistic Regression","metadata":{}},{"cell_type":"markdown","source":"From Wikipedia:\n> In statistics, the logistic model is used to model the probability of a certain class or event existing such as pass/fail, win/lose, alive/dead or healthy/sick. This can be extended to model several classes of events such as determining whether an image contains a cat, dog, lion, etc. Each object being detected in the image would be assigned a probability between 0 and 1, with a sum of one.","metadata":{}},{"cell_type":"code","source":"from sklearn.linear_model import LogisticRegression","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:44:52.409996Z","iopub.execute_input":"2021-06-08T12:44:52.410361Z","iopub.status.idle":"2021-06-08T12:44:52.414930Z","shell.execute_reply.started":"2021-06-08T12:44:52.410315Z","shell.execute_reply":"2021-06-08T12:44:52.414078Z"},"trusted":true},"execution_count":25,"outputs":[]},{"cell_type":"code","source":"lr = LogisticRegression(solver = 'saga', warm_start = True,\n                        penalty = 'elasticnet', l1_ratio = 0.3,\n                        random_state = 5, C = 1, max_iter = 500)\nlr.fit(X_train, y_train)\n\nyhat = lr.predict(X_valid)\nyhat_lr = lr.predict_proba(X_valid)\nlogloss_lr = log_loss(y_valid, yhat_lr)\nprint('Log loss using Logistic Regression:', logloss_lr)","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:44:52.416811Z","iopub.execute_input":"2021-06-08T12:44:52.417411Z","iopub.status.idle":"2021-06-08T12:46:02.969059Z","shell.execute_reply.started":"2021-06-08T12:44:52.417370Z","shell.execute_reply":"2021-06-08T12:46:02.968122Z"},"trusted":true},"execution_count":26,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n  return f(*args, **kwargs)\n","output_type":"stream"},{"name":"stdout","text":"Log loss using Logistic Regression: 0.7567071564094083\n","output_type":"stream"}]},{"cell_type":"markdown","source":"Let us use a custom function to plot our confusion matrix.","metadata":{}},{"cell_type":"code","source":"import itertools\ndef plot_confusion_matrix(cm, classes, normalize = False, title = 'Confusion matrix', cmap = plt.cm.Blues):\n    '''\n    This function prints and plots the confusion matrix. Normalization can be applied by setting normalize = True.\n    '''\n    if normalize:\n        cm = cm.astype('float')/cm.sum(axis=1)[:,np.newaxis]\n        print('Normalized Confusion matrix')\n    else:\n        print('Confusion matrix without normalization')\n    \n    print(cm)\n    plt.imshow(cm, interpolation = 'nearest', cmap = cmap)\n    plt.title(title)\n    plt.colorbar()\n    tick_marks = np.arange(len(classes))\n    plt.xticks(tick_marks, classes, rotation = 45)\n    plt.yticks(tick_marks, classes)\n    \n    if normalize:\n        fmt = '.2f'\n    else:\n        fmt = 'd'\n    \n    thresh = cm.max()/2\n    \n    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n        plt.text(j, i, format(cm[i, j], fmt), horizontalalignment = 'center',\n                color = 'white' if cm[i, j] > thresh else 'black')\n    plt.tight_layout()\n    plt.ylabel('True label')\n    plt.xlabel('Predicted label')","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:46:02.974774Z","iopub.execute_input":"2021-06-08T12:46:02.975418Z","iopub.status.idle":"2021-06-08T12:46:02.989409Z","shell.execute_reply.started":"2021-06-08T12:46:02.975376Z","shell.execute_reply":"2021-06-08T12:46:02.988225Z"},"trusted":true},"execution_count":27,"outputs":[]},{"cell_type":"code","source":"cnf_matrix = confusion_matrix(y_valid, yhat, labels = train_new.target_ord.unique().tolist())\nnp.set_printoptions(precision = 2)\n\nplt.figure()\nplot_confusion_matrix(cnf_matrix, classes = train_new.target.unique().tolist())","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:46:02.991772Z","iopub.execute_input":"2021-06-08T12:46:02.992411Z","iopub.status.idle":"2021-06-08T12:46:03.689332Z","shell.execute_reply.started":"2021-06-08T12:46:02.992372Z","shell.execute_reply":"2021-06-08T12:46:03.688500Z"},"trusted":true},"execution_count":28,"outputs":[{"name":"stdout","text":"Confusion matrix without normalization\n[[ 158   26    7    1    4   70   14  139  111]\n [   3 3741  469   64   21   26   34   37   39]\n [   0 1326  684   46    5   12   77   35   16]\n [   0  343   92  222    7   39   21    3   13]\n [   0   26    0    0  721    3    0    1    2]\n [  24   52   16   18    7 3470   55  154   91]\n [  20   98   78   22   11  103  353   79   17]\n [  50   52   17    0    5  168   46 1933   57]\n [  54   60    6    3    7   77    6   91 1059]]\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"print('Classification Report:')\nprint(classification_report(y_valid, yhat))","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:46:03.690741Z","iopub.execute_input":"2021-06-08T12:46:03.691080Z","iopub.status.idle":"2021-06-08T12:46:03.724701Z","shell.execute_reply.started":"2021-06-08T12:46:03.691042Z","shell.execute_reply":"2021-06-08T12:46:03.723743Z"},"trusted":true},"execution_count":29,"outputs":[{"name":"stdout","text":"Classification Report:\n              precision    recall  f1-score   support\n\n           0       0.51      0.30      0.38       530\n           1       0.65      0.84      0.74      4434\n           2       0.50      0.31      0.38      2201\n           3       0.59      0.30      0.40       740\n           4       0.91      0.96      0.94       753\n           5       0.87      0.89      0.88      3887\n           6       0.58      0.45      0.51       781\n           7       0.78      0.83      0.81      2328\n           8       0.75      0.78      0.77      1363\n\n    accuracy                           0.73     17017\n   macro avg       0.68      0.63      0.64     17017\nweighted avg       0.71      0.73      0.71     17017\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Using Support Vector Machines","metadata":{}},{"cell_type":"markdown","source":"From tutorialspoint:\n> An SVM model is a representation of different classes in a hyperplane in multidimensional space. The hyperplane is generated in an iterative manner by SVM so that the error can be minimized. The goal of SVM is to divide the datasets into classes to find a `maximum margin(al) hyperplane`.","metadata":{}},{"cell_type":"code","source":"from sklearn import svm","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:46:03.725958Z","iopub.execute_input":"2021-06-08T12:46:03.726309Z","iopub.status.idle":"2021-06-08T12:46:03.732167Z","shell.execute_reply.started":"2021-06-08T12:46:03.726272Z","shell.execute_reply":"2021-06-08T12:46:03.731213Z"},"trusted":true},"execution_count":30,"outputs":[]},{"cell_type":"code","source":"svm = svm.SVC(kernel = 'rbf', probability = True, random_state = 7)\nsvm.fit(X_train, y_train)\n\nyhat = svm.predict(X_valid)\nyhat_svm = svm.predict_proba(X_valid)\nlogloss_svm = log_loss(y_valid, yhat_svm)\nprint('Logloss using Support Vector Machines:', logloss_svm)","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:46:03.733208Z","iopub.execute_input":"2021-06-08T12:46:03.733495Z","iopub.status.idle":"2021-06-08T12:55:48.294408Z","shell.execute_reply.started":"2021-06-08T12:46:03.733470Z","shell.execute_reply":"2021-06-08T12:55:48.293461Z"},"trusted":true},"execution_count":31,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n  return f(*args, **kwargs)\n","output_type":"stream"},{"name":"stdout","text":"Logloss using Support Vector Machines: 0.5939131467913891\n","output_type":"stream"}]},{"cell_type":"code","source":"cnf_matrix = confusion_matrix(y_valid, yhat, labels = train_new.target_ord.unique().tolist())\n\nplt.figure()\nplot_confusion_matrix(cnf_matrix, classes = train_new.target.unique().tolist())","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:55:48.295893Z","iopub.execute_input":"2021-06-08T12:55:48.296251Z","iopub.status.idle":"2021-06-08T12:55:48.905801Z","shell.execute_reply.started":"2021-06-08T12:55:48.296212Z","shell.execute_reply":"2021-06-08T12:55:48.904982Z"},"trusted":true},"execution_count":32,"outputs":[{"name":"stdout","text":"Confusion matrix without normalization\n[[ 235   17    4    0    5   53   12   99  105]\n [   3 3897  393   37   20   14   36   14   20]\n [   0 1177  924   25    2    7   46   16    4]\n [   0  342   99  251    8   24   12    2    2]\n [   1   30    0    0  714    5    0    1    2]\n [  19   42    7    6    5 3637   40   79   52]\n [  19   97   45   16    9   58  476   52    9]\n [  41   35    4    0    3   99   18 2089   39]\n [  28   34    2    0    9   64    6   54 1166]]\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"print('Classification Report:')\nprint(classification_report(y_valid, yhat))","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:55:48.907073Z","iopub.execute_input":"2021-06-08T12:55:48.907406Z","iopub.status.idle":"2021-06-08T12:55:48.940882Z","shell.execute_reply.started":"2021-06-08T12:55:48.907367Z","shell.execute_reply":"2021-06-08T12:55:48.940000Z"},"trusted":true},"execution_count":33,"outputs":[{"name":"stdout","text":"Classification Report:\n              precision    recall  f1-score   support\n\n           0       0.68      0.44      0.54       530\n           1       0.69      0.88      0.77      4434\n           2       0.63      0.42      0.50      2201\n           3       0.75      0.34      0.47       740\n           4       0.92      0.95      0.93       753\n           5       0.92      0.94      0.93      3887\n           6       0.74      0.61      0.67       781\n           7       0.87      0.90      0.88      2328\n           8       0.83      0.86      0.84      1363\n\n    accuracy                           0.79     17017\n   macro avg       0.78      0.70      0.73     17017\nweighted avg       0.78      0.79      0.78     17017\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Using XGBoostClassifier","metadata":{}},{"cell_type":"code","source":"from xgboost import XGBClassifier","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:55:48.941998Z","iopub.execute_input":"2021-06-08T12:55:48.942481Z","iopub.status.idle":"2021-06-08T12:55:49.015791Z","shell.execute_reply.started":"2021-06-08T12:55:48.942442Z","shell.execute_reply":"2021-06-08T12:55:49.015007Z"},"trusted":true},"execution_count":34,"outputs":[]},{"cell_type":"code","source":"xgb_params = {'n_estimators': 2500,\n             'max_depth': 5,\n             'learning_rate': 0.01,\n             'min_child_weight': 4,\n             'colsample_bytree': 0.4,\n             'subsample': 0.4,\n             'reg_alpha': 0.6,\n             'reg_lambda': 0.6\n             }\nxgb = XGBClassifier(**xgb_params)\nxgb.fit(X_train, y_train, early_stopping_rounds = 5,\n       eval_set = [(X_train, y_train), (X_valid, y_valid)],\n       verbose = False)","metadata":{"execution":{"iopub.status.busy":"2021-06-08T12:55:49.016950Z","iopub.execute_input":"2021-06-08T12:55:49.017453Z","iopub.status.idle":"2021-06-08T13:13:23.177490Z","shell.execute_reply.started":"2021-06-08T12:55:49.017414Z","shell.execute_reply":"2021-06-08T13:13:23.176645Z"},"trusted":true},"execution_count":35,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/xgboost/sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n  return f(*args, **kwargs)\n","output_type":"stream"},{"name":"stdout","text":"[12:55:52] WARNING: ../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n","output_type":"stream"},{"execution_count":35,"output_type":"execute_result","data":{"text/plain":"XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=0.4, gamma=0, gpu_id=-1,\n              importance_type='gain', interaction_constraints='',\n              learning_rate=0.01, max_delta_step=0, max_depth=5,\n              min_child_weight=4, missing=nan, monotone_constraints='()',\n              n_estimators=2500, n_jobs=2, num_parallel_tree=1,\n              objective='multi:softprob', random_state=0, reg_alpha=0.6,\n              reg_lambda=0.6, scale_pos_weight=None, subsample=0.4,\n              tree_method='exact', validate_parameters=1, verbosity=None)"},"metadata":{}}]},{"cell_type":"code","source":"#To calculate log-loss, we need the probability of each prediction\nyhat_xgbc = xgb.predict_proba(X_valid)\nlogloss_XGBC = log_loss(y_valid, yhat_xgbc)\nprint(\"Log loss using XGB Classifier:\", logloss_XGBC)","metadata":{"execution":{"iopub.status.busy":"2021-06-08T13:13:23.178898Z","iopub.execute_input":"2021-06-08T13:13:23.179274Z","iopub.status.idle":"2021-06-08T13:13:29.533119Z","shell.execute_reply.started":"2021-06-08T13:13:23.179233Z","shell.execute_reply":"2021-06-08T13:13:29.532181Z"},"trusted":true},"execution_count":36,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/xgboost/data.py:114: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n  \"because it will generate extra copies and increase \" +\n","output_type":"stream"},{"name":"stdout","text":"Log loss using XGB Classifier: 0.5138227791530505\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Using the XGBoost Feature Importance Plot","metadata":{}},{"cell_type":"code","source":"from xgboost import plot_importance\n# Plot feature importance\nax = plot_importance(xgb, max_num_features=12, show_values=True) \nfig = ax.figure\nfig.set_size_inches(10, 3)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-06-08T13:13:29.534369Z","iopub.execute_input":"2021-06-08T13:13:29.534742Z","iopub.status.idle":"2021-06-08T13:13:33.608675Z","shell.execute_reply.started":"2021-06-08T13:13:29.534687Z","shell.execute_reply":"2021-06-08T13:13:33.607665Z"},"trusted":true},"execution_count":37,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 720x216 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"results = xgb.evals_result()","metadata":{"execution":{"iopub.status.busy":"2021-06-08T13:13:33.610027Z","iopub.execute_input":"2021-06-08T13:13:33.610382Z","iopub.status.idle":"2021-06-08T13:13:33.615049Z","shell.execute_reply.started":"2021-06-08T13:13:33.610342Z","shell.execute_reply":"2021-06-08T13:13:33.613822Z"},"trusted":true},"execution_count":38,"outputs":[]},{"cell_type":"code","source":"# Plot learning curves\nplt.plot(results['validation_0']['mlogloss'], label='train')\nplt.plot(results['validation_1']['mlogloss'], label='test')\nplt.legend()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-06-08T13:13:33.616532Z","iopub.execute_input":"2021-06-08T13:13:33.616994Z","iopub.status.idle":"2021-06-08T13:13:33.798403Z","shell.execute_reply.started":"2021-06-08T13:13:33.616956Z","shell.execute_reply":"2021-06-08T13:13:33.797574Z"},"trusted":true},"execution_count":39,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"## Using AdaBoostClassifier","metadata":{}},{"cell_type":"code","source":"from sklearn.ensemble import AdaBoostClassifier","metadata":{"execution":{"iopub.status.busy":"2021-06-08T13:13:33.799711Z","iopub.execute_input":"2021-06-08T13:13:33.800065Z","iopub.status.idle":"2021-06-08T13:13:33.804284Z","shell.execute_reply.started":"2021-06-08T13:13:33.800026Z","shell.execute_reply":"2021-06-08T13:13:33.803213Z"},"trusted":true},"execution_count":40,"outputs":[]},{"cell_type":"code","source":"abc = AdaBoostClassifier(n_estimators = 1000, random_state = 0, learning_rate = 0.12)\nabc.fit(X_train, y_train)\n\nyhat_ABC = abc.predict_proba(X_valid)\nlogloss_ABC = log_loss(y_valid, yhat_ABC)\nprint('Log loss using Ada Boost Classifier:', logloss_ABC)","metadata":{"execution":{"iopub.status.busy":"2021-06-08T13:13:33.805644Z","iopub.execute_input":"2021-06-08T13:13:33.805998Z","iopub.status.idle":"2021-06-08T13:14:51.988903Z","shell.execute_reply.started":"2021-06-08T13:13:33.805960Z","shell.execute_reply":"2021-06-08T13:14:51.987918Z"},"trusted":true},"execution_count":41,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n  return f(*args, **kwargs)\n","output_type":"stream"},{"name":"stdout","text":"Log loss using Ada Boost Classifier: 2.1159445210915195\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Using CatBoostClassifier","metadata":{}},{"cell_type":"markdown","source":"The following parameters have been set by trial and error from [Parameter tuning](https://catboost.ai/docs/concepts/parameter-tuning.html) and [Speeding up training](https://catboost.ai/docs/concepts/speed-up-training.html).","metadata":{}},{"cell_type":"code","source":"from catboost import CatBoostClassifier","metadata":{"execution":{"iopub.status.busy":"2021-06-08T13:14:51.990202Z","iopub.execute_input":"2021-06-08T13:14:51.990706Z","iopub.status.idle":"2021-06-08T13:14:52.011788Z","shell.execute_reply.started":"2021-06-08T13:14:51.990663Z","shell.execute_reply":"2021-06-08T13:14:52.010972Z"},"trusted":true},"execution_count":42,"outputs":[]},{"cell_type":"code","source":"CBC_params = {\n                'iterations': 5000, \n                'od_wait': 250,\n                'use_best_model': True,\n                'loss_function': 'MultiClass',\n                'eval_metric': 'MultiClass',\n                'leaf_estimation_method': 'Newton',\n                'bootstrap_type': 'Bernoulli',\n                'subsample': 0.4,\n                'learning_rate': 0.05,\n                'l2_leaf_reg': 0.5, #L2 Regularization\n                'random_strength': 10, #amount of randomness to use for scoring splits when tree structure is selected\n                'depth': 6, #Tree depth\n                'min_data_in_leaf': 3, #minimum number of training samples in a leaf\n                'leaf_estimation_iterations': 4, #Earlier = 7\n                'task_type': 'GPU',\n                'border_count': 128, #Number of splits for numerical features\n                'grow_policy': 'SymmetricTree'\n            }","metadata":{"execution":{"iopub.status.busy":"2021-06-08T13:14:52.012987Z","iopub.execute_input":"2021-06-08T13:14:52.013365Z","iopub.status.idle":"2021-06-08T13:14:52.019547Z","shell.execute_reply.started":"2021-06-08T13:14:52.013323Z","shell.execute_reply":"2021-06-08T13:14:52.018302Z"},"trusted":true},"execution_count":43,"outputs":[]},{"cell_type":"code","source":"cbc = CatBoostClassifier(**CBC_params)\ncbc.fit(X_train, y_train,\n       eval_set = [(X_valid, y_valid)],\n       early_stopping_rounds = 20,\n       verbose = False)","metadata":{"execution":{"iopub.status.busy":"2021-06-08T13:14:52.020975Z","iopub.execute_input":"2021-06-08T13:14:52.021434Z","iopub.status.idle":"2021-06-08T13:16:23.834041Z","shell.execute_reply.started":"2021-06-08T13:14:52.021396Z","shell.execute_reply":"2021-06-08T13:16:23.833260Z"},"trusted":true},"execution_count":44,"outputs":[{"execution_count":44,"output_type":"execute_result","data":{"text/plain":"<catboost.core.CatBoostClassifier at 0x7f906346f390>"},"metadata":{}}]},{"cell_type":"code","source":"yhat_CBC = cbc.predict_proba(X_valid)\nlogloss_CBC = log_loss(y_valid, yhat_CBC)\nprint('Log loss using CatBoost Classifier:', logloss_CBC)","metadata":{"execution":{"iopub.status.busy":"2021-06-08T13:16:23.837419Z","iopub.execute_input":"2021-06-08T13:16:23.839188Z","iopub.status.idle":"2021-06-08T13:16:24.195693Z","shell.execute_reply.started":"2021-06-08T13:16:23.839150Z","shell.execute_reply":"2021-06-08T13:16:24.194773Z"},"trusted":true},"execution_count":45,"outputs":[{"name":"stdout","text":"Log loss using CatBoost Classifier: 0.49187321346251833\n","output_type":"stream"}]}]}