random_forest_regression(done).ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1LbKk9jruYgVclaVUTtSRiFiv6uQN55pD","timestamp":1730853629934}]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"LeamvpPVXuS_"},"source":["# Random Forest Regression"]},{"cell_type":"markdown","metadata":{"id":"O2wvZ7SKXzVC"},"source":["## Importing the libraries"]},{"cell_type":"code","source":["import numpy as np\n","import matplotlib.pyplot as plt\n","import pandas as pd"],"metadata":{"id":"nwUBow_C6yPK","executionInfo":{"status":"ok","timestamp":1730854159235,"user_tz":-420,"elapsed":441,"user":{"displayName":"hưng hoàng","userId":"09018587544555875926"}}},"execution_count":2,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"zgbK_F8-X7em"},"source":["## Importing the dataset"]},{"cell_type":"code","source":["dataset = pd.read_csv('Position_Salaries.csv')\n","x = dataset.iloc[:,1:-1].values\n","y = dataset.iloc[:,-1].values"],"metadata":{"id":"A0zZjrOq6ymM","executionInfo":{"status":"ok","timestamp":1730854159570,"user_tz":-420,"elapsed":2,"user":{"displayName":"hưng hoàng","userId":"09018587544555875926"}}},"execution_count":3,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"v4S2fyIBYDcu"},"source":["## Training the Random Forest Regression model on the whole dataset"]},{"cell_type":"code","source":["from sklearn.ensemble import RandomForestRegressor\n","regressor = RandomForestRegressor(n_estimators= 10,random_state = 10)\n","regressor.fit(x,y)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"kqBkDPbk6y9T","executionInfo":{"status":"ok","timestamp":1730854180683,"user_tz":-420,"elapsed":324,"user":{"displayName":"hưng hoàng","userId":"09018587544555875926"}},"outputId":"e4ca63f0-8971-41ec-b5a0-a0f8587783ea"},"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["RandomForestRegressor(n_estimators=10, random_state=10)"],"text/html":["<style>#sk-container-id-1 {\n","  /* Definition of color scheme common for light and dark mode */\n","  --sklearn-color-text: black;\n","  --sklearn-color-line: gray;\n","  /* Definition of color scheme for unfitted estimators */\n","  --sklearn-color-unfitted-level-0: #fff5e6;\n","  --sklearn-color-unfitted-level-1: #f6e4d2;\n","  --sklearn-color-unfitted-level-2: #ffe0b3;\n","  --sklearn-color-unfitted-level-3: chocolate;\n","  /* Definition of color scheme for fitted estimators */\n","  --sklearn-color-fitted-level-0: #f0f8ff;\n","  --sklearn-color-fitted-level-1: #d4ebff;\n","  --sklearn-color-fitted-level-2: #b3dbfd;\n","  --sklearn-color-fitted-level-3: cornflowerblue;\n","\n","  /* Specific color for light theme */\n","  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n","  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-icon: #696969;\n","\n","  @media (prefers-color-scheme: dark) {\n","    /* Redefinition of color scheme for dark theme */\n","    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n","    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-icon: #878787;\n","  }\n","}\n","\n","#sk-container-id-1 {\n","  color: var(--sklearn-color-text);\n","}\n","\n","#sk-container-id-1 pre {\n","  padding: 0;\n","}\n","\n","#sk-container-id-1 input.sk-hidden--visually {\n","  border: 0;\n","  clip: rect(1px 1px 1px 1px);\n","  clip: rect(1px, 1px, 1px, 1px);\n","  height: 1px;\n","  margin: -1px;\n","  overflow: hidden;\n","  padding: 0;\n","  position: absolute;\n","  width: 1px;\n","}\n","\n","#sk-container-id-1 div.sk-dashed-wrapped {\n","  border: 1px dashed var(--sklearn-color-line);\n","  margin: 0 0.4em 0.5em 0.4em;\n","  box-sizing: border-box;\n","  padding-bottom: 0.4em;\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","#sk-container-id-1 div.sk-container {\n","  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n","     but bootstrap.min.css set `[hidden] { display: none !important; }`\n","     so we also need the `!important` here to be able to override the\n","     default hidden behavior on the sphinx rendered scikit-learn.org.\n","     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n","  display: inline-block !important;\n","  position: relative;\n","}\n","\n","#sk-container-id-1 div.sk-text-repr-fallback {\n","  display: none;\n","}\n","\n","div.sk-parallel-item,\n","div.sk-serial,\n","div.sk-item {\n","  /* draw centered vertical line to link estimators */\n","  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n","  background-size: 2px 100%;\n","  background-repeat: no-repeat;\n","  background-position: center center;\n","}\n","\n","/* Parallel-specific style estimator block */\n","\n","#sk-container-id-1 div.sk-parallel-item::after {\n","  content: \"\";\n","  width: 100%;\n","  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n","  flex-grow: 1;\n","}\n","\n","#sk-container-id-1 div.sk-parallel {\n","  display: flex;\n","  align-items: stretch;\n","  justify-content: center;\n","  background-color: var(--sklearn-color-background);\n","  position: relative;\n","}\n","\n","#sk-container-id-1 div.sk-parallel-item {\n","  display: flex;\n","  flex-direction: column;\n","}\n","\n","#sk-container-id-1 div.sk-parallel-item:first-child::after {\n","  align-self: flex-end;\n","  width: 50%;\n","}\n","\n","#sk-container-id-1 div.sk-parallel-item:last-child::after {\n","  align-self: flex-start;\n","  width: 50%;\n","}\n","\n","#sk-container-id-1 div.sk-parallel-item:only-child::after {\n","  width: 0;\n","}\n","\n","/* Serial-specific style estimator block */\n","\n","#sk-container-id-1 div.sk-serial {\n","  display: flex;\n","  flex-direction: column;\n","  align-items: center;\n","  background-color: var(--sklearn-color-background);\n","  padding-right: 1em;\n","  padding-left: 1em;\n","}\n","\n","\n","/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n","clickable and can be expanded/collapsed.\n","- Pipeline and ColumnTransformer use this feature and define the default style\n","- Estimators will overwrite some part of the style using the `sk-estimator` class\n","*/\n","\n","/* Pipeline and ColumnTransformer style (default) */\n","\n","#sk-container-id-1 div.sk-toggleable {\n","  /* Default theme specific background. It is overwritten whether we have a\n","  specific estimator or a Pipeline/ColumnTransformer */\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","/* Toggleable label */\n","#sk-container-id-1 label.sk-toggleable__label {\n","  cursor: pointer;\n","  display: block;\n","  width: 100%;\n","  margin-bottom: 0;\n","  padding: 0.5em;\n","  box-sizing: border-box;\n","  text-align: center;\n","}\n","\n","#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n","  /* Arrow on the left of the label */\n","  content: \"▸\";\n","  float: left;\n","  margin-right: 0.25em;\n","  color: var(--sklearn-color-icon);\n","}\n","\n","#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n","  color: var(--sklearn-color-text);\n","}\n","\n","/* Toggleable content - dropdown */\n","\n","#sk-container-id-1 div.sk-toggleable__content {\n","  max-height: 0;\n","  max-width: 0;\n","  overflow: hidden;\n","  text-align: left;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-1 div.sk-toggleable__content.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-1 div.sk-toggleable__content pre {\n","  margin: 0.2em;\n","  border-radius: 0.25em;\n","  color: var(--sklearn-color-text);\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n","  /* Expand drop-down */\n","  max-height: 200px;\n","  max-width: 100%;\n","  overflow: auto;\n","}\n","\n","#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n","  content: \"▾\";\n","}\n","\n","/* Pipeline/ColumnTransformer-specific style */\n","\n","#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator-specific style */\n","\n","/* Colorize estimator box */\n","#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n","#sk-container-id-1 div.sk-label label {\n","  /* The background is the default theme color */\n","  color: var(--sklearn-color-text-on-default-background);\n","}\n","\n","/* On hover, darken the color of the background */\n","#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","/* Label box, darken color on hover, fitted */\n","#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator label */\n","\n","#sk-container-id-1 div.sk-label label {\n","  font-family: monospace;\n","  font-weight: bold;\n","  display: inline-block;\n","  line-height: 1.2em;\n","}\n","\n","#sk-container-id-1 div.sk-label-container {\n","  text-align: center;\n","}\n","\n","/* Estimator-specific */\n","#sk-container-id-1 div.sk-estimator {\n","  font-family: monospace;\n","  border: 1px dotted var(--sklearn-color-border-box);\n","  border-radius: 0.25em;\n","  box-sizing: border-box;\n","  margin-bottom: 0.5em;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-1 div.sk-estimator.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","/* on hover */\n","#sk-container-id-1 div.sk-estimator:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-1 div.sk-estimator.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Specification for estimator info (e.g. \"i\" and \"?\") */\n","\n","/* Common style for \"i\" and \"?\" */\n","\n",".sk-estimator-doc-link,\n","a:link.sk-estimator-doc-link,\n","a:visited.sk-estimator-doc-link {\n","  float: right;\n","  font-size: smaller;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1em;\n","  height: 1em;\n","  width: 1em;\n","  text-decoration: none !important;\n","  margin-left: 1ex;\n","  /* unfitted */\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-unfitted-level-1);\n","}\n","\n",".sk-estimator-doc-link.fitted,\n","a:link.sk-estimator-doc-link.fitted,\n","a:visited.sk-estimator-doc-link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","/* Span, style for the box shown on hovering the info icon */\n",".sk-estimator-doc-link span {\n","  display: none;\n","  z-index: 9999;\n","  position: relative;\n","  font-weight: normal;\n","  right: .2ex;\n","  padding: .5ex;\n","  margin: .5ex;\n","  width: min-content;\n","  min-width: 20ex;\n","  max-width: 50ex;\n","  color: var(--sklearn-color-text);\n","  box-shadow: 2pt 2pt 4pt #999;\n","  /* unfitted */\n","  background: var(--sklearn-color-unfitted-level-0);\n","  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n","}\n","\n",".sk-estimator-doc-link.fitted span {\n","  /* fitted */\n","  background: var(--sklearn-color-fitted-level-0);\n","  border: var(--sklearn-color-fitted-level-3);\n","}\n","\n",".sk-estimator-doc-link:hover span {\n","  display: block;\n","}\n","\n","/* \"?\"-specific style due to the `<a>` HTML tag */\n","\n","#sk-container-id-1 a.estimator_doc_link {\n","  float: right;\n","  font-size: 1rem;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1rem;\n","  height: 1rem;\n","  width: 1rem;\n","  text-decoration: none;\n","  /* unfitted */\n","  color: var(--sklearn-color-unfitted-level-1);\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","}\n","\n","#sk-container-id-1 a.estimator_doc_link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","#sk-container-id-1 a.estimator_doc_link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","}\n","</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(n_estimators=10, random_state=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(n_estimators=10, random_state=10)</pre></div> </div></div></div></div>"]},"metadata":{},"execution_count":5}]},{"cell_type":"markdown","metadata":{"id":"8IbsXbK3YM4M"},"source":["## Predicting a new result"]},{"cell_type":"code","source":["regressor.predict([[6.5]])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0Qt1asHo6zaM","executionInfo":{"status":"ok","timestamp":1730854182802,"user_tz":-420,"elapsed":336,"user":{"displayName":"hưng hoàng","userId":"09018587544555875926"}},"outputId":"c0d22992-10dd-4eb4-a581-61284c8f0607"},"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([170000.])"]},"metadata":{},"execution_count":6}]},{"cell_type":"markdown","metadata":{"id":"kLqF9yMbYTon"},"source":["## Visualising the Random Forest Regression results (higher resolution)"]},{"cell_type":"code","source":["x_grid = np.arange(min(x), max(x), 0.01)\n","x_grid = x_grid.reshape((len(x_grid), 1))\n","plt.scatter(x, y, color = 'red')\n","plt.plot(x_grid, regressor.predict(x_grid), color = 'blue')\n","plt.title('Truth or Bluff (Random Forest Regression)')\n","plt.xlabel('Position level')\n","plt.ylabel('Salary')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":527},"id":"vkubo2YB6zvR","executionInfo":{"status":"ok","timestamp":1730854220774,"user_tz":-420,"elapsed":381,"user":{"displayName":"hưng hoàng","userId":"09018587544555875926"}},"outputId":"3481e693-df87-4965-dcd2-de97d88b2976"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-8-432605427593>:1: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n","  x_grid = np.arange(min(x), max(x), 0.01)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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\n"},"metadata":{}}]}]}