Prediction of leaderboard score (R).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction of leaderboard score (R)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>model</th><th scope=col>leaderboard_score</th><th scope=col>accuracy</th><th scope=col>logloss</th><th scope=col>AUC</th><th scope=col>f1</th><th scope=col>mu</th><th scope=col>std</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>19</th><td>bagged_nolearn             </td><td>0.4313</td><td>0.7813</td><td>7.5554</td><td>0.5857</td><td>0.3152</td><td>NA</td><td>NA</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>ensemble of averages       </td><td>0.437</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
       "\t<tr><th scope=row>16</th><td>voting_ensemble_softWgtd   </td><td>0.4396</td><td>0.7865</td><td>7.3755</td><td>0.6065</td><td>0.3692</td><td>0.76</td><td>0.0013</td></tr>\n",
       "\t<tr><th scope=row>20</th><td>LogisticRegression         </td><td>0.4411</td><td>0.7708</td><td>7.9152</td><td>0.554</td><td>0.2235</td><td>0.7601</td><td>0.0013</td></tr>\n",
       "\t<tr><th scope=row>22</th><td>bagged_logit               </td><td>0.4442</td><td>0.783</td><td>7.4954</td><td>0.5794</td><td>0.2938</td><td>0.76</td><td>0.0015</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>GradientBoostingClassifier </td><td>0.4452</td><td>0.7934</td><td>7.1356</td><td>0.6309</td><td>0.4251</td><td>0.7538</td><td>0.0047</td></tr>\n",
       "\t<tr><th scope=row>21</th><td>LogisticRegressionCV       </td><td>0.4457</td><td>0.783</td><td>7.4954</td><td>0.5794</td><td>0.2938</td><td>0.7602</td><td>0.0012</td></tr>\n",
       "\t<tr><th scope=row>25</th><td>bagged_scikit_nn           </td><td>0.4465</td><td>0.7986</td><td>6.9558</td><td>0.674</td><td>0.5085</td><td>0.7463</td><td>0.0065</td></tr>\n",
       "\t<tr><th scope=row>17</th><td>bagged_gbc                 </td><td>0.4527</td><td>0.7899</td><td>7.2556</td><td>0.6137</td><td>0.3858</td><td>0.7573</td><td>0.0037</td></tr>\n",
       "\t<tr><th scope=row>1</th><td>nolearn                    </td><td>0.4566</td><td>0.8056</td><td>6.7159</td><td>0.6711</td><td>0.5044</td><td>NA</td><td>NA</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>ExtraTreesClassifier       </td><td>0.4729</td><td>0.776</td><td>7.7353</td><td>0.5996</td><td>0.3582</td><td>0.7526</td><td>0.0061</td></tr>\n",
       "\t<tr><th scope=row>26</th><td>blending_ensemble          </td><td>0.4834</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>XGBClassifier              </td><td>0.4851</td><td>0.7743</td><td>7.7953</td><td>0.6034</td><td>0.3689</td><td>0.7474</td><td>0.0065</td></tr>\n",
       "\t<tr><th scope=row>13</th><td>BaggingClassifier          </td><td>0.4885</td><td>0.7743</td><td>7.7952</td><td>0.5985</td><td>0.3564</td><td>0.7523</td><td>0.0051</td></tr>\n",
       "\t<tr><th scope=row>24</th><td>scikit_nn                  </td><td>0.502</td><td>0.7969</td><td>7.0157</td><td>0.6803</td><td>0.5185</td><td>0.7387</td><td>0.0116</td></tr>\n",
       "\t<tr><th scope=row>18</th><td>boosted_svc                </td><td>0.5334</td><td>0.7691</td><td>7.9751</td><td>0.5206</td><td>0.0828</td><td>0.7602</td><td>0.0012</td></tr>\n",
       "\t<tr><th scope=row>11</th><td>SVC                        </td><td>0.5336</td><td>0.7552</td><td>8.4548</td><td>0.5264</td><td>0.1455</td><td>0.742</td><td>0.0079</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>SGDClassifier              </td><td>0.567</td><td>0.7274</td><td>9.4143</td><td>0.5528</td><td>0.2765</td><td>0.6339</td><td>0.069</td></tr>\n",
       "\t<tr><th scope=row>7</th><td>cosine_similarity          </td><td>0.5732</td><td>0.7906</td><td>7.2333</td><td>0.6452</td><td>0.4595</td><td>NA</td><td>NA</td></tr>\n",
       "\t<tr><th scope=row>23</th><td>boosted_logit              </td><td>0.5891</td><td>0.7708</td><td>7.9152</td><td>0.5788</td><td>0.3053</td><td>0.7589</td><td>0.002</td></tr>\n",
       "\t<tr><th scope=row>12</th><td>KMeans                     </td><td>0.6289</td><td>0.6892</td><td>10.7335</td><td>0.5425</td><td>0.2869</td><td>NA</td><td>NA</td></tr>\n",
       "\t<tr><th scope=row>8</th><td>AdaBoostClassifier         </td><td>0.6642</td><td>0.783</td><td>7.4954</td><td>0.6191</td><td>0.4019</td><td>0.7574</td><td>0.0036</td></tr>\n",
       "\t<tr><th scope=row>9</th><td>KNeighborsClassifier       </td><td>1.187</td><td>0.7778</td><td>7.6753</td><td>0.6206</td><td>0.4074</td><td>0.7387</td><td>0.0086</td></tr>\n",
       "\t<tr><th scope=row>10</th><td>RandomForestClassifier     </td><td>1.7907</td><td>0.7344</td><td>9.1744</td><td>0.5722</td><td>0.32</td><td>0.6954</td><td>0.0149</td></tr>\n",
       "\t<tr><th scope=row>14</th><td>voting_ensemble_hard       </td><td>NA</td><td>0.7882</td><td>7.3155</td><td>0.6076</td><td>0.3711</td><td>0.7594</td><td>0.0017</td></tr>\n",
       "\t<tr><th scope=row>15</th><td>voting_ensemble_hardWgtd   </td><td>NA</td><td>0.7813</td><td>7.5554</td><td>0.5733</td><td>0.2759</td><td>0.76</td><td>0.0012</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllll}\n",
       "  & model & leaderboard_score & accuracy & logloss & AUC & f1 & mu & std\\\\\n",
       "\\hline\n",
       "\t19 & bagged_nolearn              & 0.4313 & 0.7813 & 7.5554 & 0.5857 & 0.3152 & NA & NA\\\\\n",
       "\t2 & ensemble of averages        & 0.437 & NA & NA & NA & NA & NA & NA\\\\\n",
       "\t16 & voting_ensemble_softWgtd    & 0.4396 & 0.7865 & 7.3755 & 0.6065 & 0.3692 & 0.76 & 0.0013\\\\\n",
       "\t20 & LogisticRegression          & 0.4411 & 0.7708 & 7.9152 & 0.554 & 0.2235 & 0.7601 & 0.0013\\\\\n",
       "\t22 & bagged_logit                & 0.4442 & 0.783 & 7.4954 & 0.5794 & 0.2938 & 0.76 & 0.0015\\\\\n",
       "\t3 & GradientBoostingClassifier  & 0.4452 & 0.7934 & 7.1356 & 0.6309 & 0.4251 & 0.7538 & 0.0047\\\\\n",
       "\t21 & LogisticRegressionCV        & 0.4457 & 0.783 & 7.4954 & 0.5794 & 0.2938 & 0.7602 & 0.0012\\\\\n",
       "\t25 & bagged_scikit_nn            & 0.4465 & 0.7986 & 6.9558 & 0.674 & 0.5085 & 0.7463 & 0.0065\\\\\n",
       "\t17 & bagged_gbc                  & 0.4527 & 0.7899 & 7.2556 & 0.6137 & 0.3858 & 0.7573 & 0.0037\\\\\n",
       "\t1 & nolearn                     & 0.4566 & 0.8056 & 6.7159 & 0.6711 & 0.5044 & NA & NA\\\\\n",
       "\t4 & ExtraTreesClassifier        & 0.4729 & 0.776 & 7.7353 & 0.5996 & 0.3582 & 0.7526 & 0.0061\\\\\n",
       "\t26 & blending_ensemble           & 0.4834 & NA & NA & NA & NA & NA & NA\\\\\n",
       "\t5 & XGBClassifier               & 0.4851 & 0.7743 & 7.7953 & 0.6034 & 0.3689 & 0.7474 & 0.0065\\\\\n",
       "\t13 & BaggingClassifier           & 0.4885 & 0.7743 & 7.7952 & 0.5985 & 0.3564 & 0.7523 & 0.0051\\\\\n",
       "\t24 & scikit_nn                   & 0.502 & 0.7969 & 7.0157 & 0.6803 & 0.5185 & 0.7387 & 0.0116\\\\\n",
       "\t18 & boosted_svc                 & 0.5334 & 0.7691 & 7.9751 & 0.5206 & 0.0828 & 0.7602 & 0.0012\\\\\n",
       "\t11 & SVC                         & 0.5336 & 0.7552 & 8.4548 & 0.5264 & 0.1455 & 0.742 & 0.0079\\\\\n",
       "\t6 & SGDClassifier               & 0.567 & 0.7274 & 9.4143 & 0.5528 & 0.2765 & 0.6339 & 0.069\\\\\n",
       "\t7 & cosine_similarity           & 0.5732 & 0.7906 & 7.2333 & 0.6452 & 0.4595 & NA & NA\\\\\n",
       "\t23 & boosted_logit               & 0.5891 & 0.7708 & 7.9152 & 0.5788 & 0.3053 & 0.7589 & 0.002\\\\\n",
       "\t12 & KMeans                      & 0.6289 & 0.6892 & 10.7335 & 0.5425 & 0.2869 & NA & NA\\\\\n",
       "\t8 & AdaBoostClassifier          & 0.6642 & 0.783 & 7.4954 & 0.6191 & 0.4019 & 0.7574 & 0.0036\\\\\n",
       "\t9 & KNeighborsClassifier        & 1.187 & 0.7778 & 7.6753 & 0.6206 & 0.4074 & 0.7387 & 0.0086\\\\\n",
       "\t10 & RandomForestClassifier      & 1.7907 & 0.7344 & 9.1744 & 0.5722 & 0.32 & 0.6954 & 0.0149\\\\\n",
       "\t14 & voting_ensemble_hard        & NA & 0.7882 & 7.3155 & 0.6076 & 0.3711 & 0.7594 & 0.0017\\\\\n",
       "\t15 & voting_ensemble_hardWgtd    & NA & 0.7813 & 7.5554 & 0.5733 & 0.2759 & 0.76 & 0.0012\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "                         model leaderboard_score accuracy logloss    AUC     f1\n",
       "19 bagged_nolearn                         0.4313   0.7813  7.5554 0.5857 0.3152\n",
       "2  ensemble of averages                   0.4370       NA      NA     NA     NA\n",
       "16 voting_ensemble_softWgtd               0.4396   0.7865  7.3755 0.6065 0.3692\n",
       "20 LogisticRegression                     0.4411   0.7708  7.9152 0.5540 0.2235\n",
       "22 bagged_logit                           0.4442   0.7830  7.4954 0.5794 0.2938\n",
       "3  GradientBoostingClassifier             0.4452   0.7934  7.1356 0.6309 0.4251\n",
       "21 LogisticRegressionCV                   0.4457   0.7830  7.4954 0.5794 0.2938\n",
       "25 bagged_scikit_nn                       0.4465   0.7986  6.9558 0.6740 0.5085\n",
       "17 bagged_gbc                             0.4527   0.7899  7.2556 0.6137 0.3858\n",
       "1  nolearn                                0.4566   0.8056  6.7159 0.6711 0.5044\n",
       "4  ExtraTreesClassifier                   0.4729   0.7760  7.7353 0.5996 0.3582\n",
       "26 blending_ensemble                      0.4834       NA      NA     NA     NA\n",
       "5  XGBClassifier                          0.4851   0.7743  7.7953 0.6034 0.3689\n",
       "13 BaggingClassifier                      0.4885   0.7743  7.7952 0.5985 0.3564\n",
       "24 scikit_nn                              0.5020   0.7969  7.0157 0.6803 0.5185\n",
       "18 boosted_svc                            0.5334   0.7691  7.9751 0.5206 0.0828\n",
       "11 SVC                                    0.5336   0.7552  8.4548 0.5264 0.1455\n",
       "6  SGDClassifier                          0.5670   0.7274  9.4143 0.5528 0.2765\n",
       "7  cosine_similarity                      0.5732   0.7906  7.2333 0.6452 0.4595\n",
       "23 boosted_logit                          0.5891   0.7708  7.9152 0.5788 0.3053\n",
       "12 KMeans                                 0.6289   0.6892 10.7335 0.5425 0.2869\n",
       "8  AdaBoostClassifier                     0.6642   0.7830  7.4954 0.6191 0.4019\n",
       "9  KNeighborsClassifier                   1.1870   0.7778  7.6753 0.6206 0.4074\n",
       "10 RandomForestClassifier                 1.7907   0.7344  9.1744 0.5722 0.3200\n",
       "14 voting_ensemble_hard                       NA   0.7882  7.3155 0.6076 0.3711\n",
       "15 voting_ensemble_hardWgtd                   NA   0.7813  7.5554 0.5733 0.2759\n",
       "       mu    std\n",
       "19     NA     NA\n",
       "2      NA     NA\n",
       "16 0.7600 0.0013\n",
       "20 0.7601 0.0013\n",
       "22 0.7600 0.0015\n",
       "3  0.7538 0.0047\n",
       "21 0.7602 0.0012\n",
       "25 0.7463 0.0065\n",
       "17 0.7573 0.0037\n",
       "1      NA     NA\n",
       "4  0.7526 0.0061\n",
       "26     NA     NA\n",
       "5  0.7474 0.0065\n",
       "13 0.7523 0.0051\n",
       "24 0.7387 0.0116\n",
       "18 0.7602 0.0012\n",
       "11 0.7420 0.0079\n",
       "6  0.6339 0.0690\n",
       "7      NA     NA\n",
       "23 0.7589 0.0020\n",
       "12     NA     NA\n",
       "8  0.7574 0.0036\n",
       "9  0.7387 0.0086\n",
       "10 0.6954 0.0149\n",
       "14 0.7594 0.0017\n",
       "15 0.7600 0.0012"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_data = read.csv('../input/scores.csv',stringsAsFactors=FALSE)\n",
    "score_data[with(score_data, order(leaderboard_score)), ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model using all variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start:  AIC=-60.18\n",
      "leaderboard_score ~ accuracy + logloss + AUC + f1 + mu + std\n",
      "\n",
      "           Df Sum of Sq     RSS     AIC\n",
      "- AUC       1  0.000051 0.29209 -62.179\n",
      "- f1        1  0.004574 0.29662 -61.902\n",
      "- accuracy  1  0.022137 0.31418 -60.867\n",
      "- logloss   1  0.022258 0.31430 -60.860\n",
      "<none>                  0.29204 -60.182\n",
      "- mu        1  0.107786 0.39983 -56.528\n",
      "- std       1  0.254788 0.54683 -50.892\n",
      "\n",
      "Step:  AIC=-62.18\n",
      "leaderboard_score ~ accuracy + logloss + f1 + mu + std\n",
      "\n",
      "           Df Sum of Sq     RSS     AIC\n",
      "- accuracy  1   0.02267 0.31476 -62.834\n",
      "- logloss   1   0.02282 0.31492 -62.825\n",
      "- f1        1   0.03171 0.32380 -62.324\n",
      "<none>                  0.29209 -62.179\n",
      "+ AUC       1   0.00005 0.29204 -60.182\n",
      "- mu        1   0.15368 0.44577 -56.570\n",
      "- std       1   0.32770 0.61980 -50.637\n",
      "\n",
      "Step:  AIC=-62.83\n",
      "leaderboard_score ~ logloss + f1 + mu + std\n",
      "\n",
      "           Df Sum of Sq     RSS     AIC\n",
      "- logloss   1   0.02081 0.33557 -63.681\n",
      "- f1        1   0.02103 0.33579 -63.669\n",
      "<none>                  0.31476 -62.834\n",
      "+ accuracy  1   0.02267 0.29209 -62.179\n",
      "+ AUC       1   0.00058 0.31418 -60.867\n",
      "- mu        1   0.30384 0.61860 -52.672\n",
      "- std       1   0.62665 0.94141 -45.114\n",
      "\n",
      "Step:  AIC=-63.68\n",
      "leaderboard_score ~ f1 + mu + std\n",
      "\n",
      "           Df Sum of Sq     RSS     AIC\n",
      "- f1        1   0.00139 0.33695 -65.607\n",
      "<none>                  0.33557 -63.681\n",
      "+ logloss   1   0.02081 0.31476 -62.834\n",
      "+ accuracy  1   0.02065 0.31492 -62.825\n",
      "+ AUC       1   0.01626 0.31931 -62.575\n",
      "- std       1   1.33496 1.67052 -36.790\n",
      "- mu        1   1.61961 1.95517 -33.958\n",
      "\n",
      "Step:  AIC=-65.61\n",
      "leaderboard_score ~ mu + std\n",
      "\n",
      "           Df Sum of Sq     RSS     AIC\n",
      "<none>                  0.33695 -65.607\n",
      "+ f1        1   0.00139 0.33557 -63.681\n",
      "+ logloss   1   0.00116 0.33579 -63.669\n",
      "+ accuracy  1   0.00115 0.33581 -63.669\n",
      "+ AUC       1   0.00017 0.33678 -63.616\n",
      "- std       1   1.33570 1.67265 -38.767\n",
      "- mu        1   1.61917 1.95612 -35.949\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = leaderboard_score ~ mu + std, data = score_data, \n",
       "    na.action = na.omit)\n",
       "\n",
       "Residuals:\n",
       "     Min       1Q   Median       3Q      Max \n",
       "-0.18728 -0.05472 -0.03539  0.02082  0.42898 \n",
       "\n",
       "Coefficients:\n",
       "            Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)   25.722      2.962   8.685 3.09e-07 ***\n",
       "mu           -33.089      3.897  -8.490 4.11e-07 ***\n",
       "std          -60.589      7.857  -7.711 1.35e-06 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 0.1499 on 15 degrees of freedom\n",
       "  (8 observations deleted due to missingness)\n",
       "Multiple R-squared:  0.8311,\tAdjusted R-squared:  0.8086 \n",
       "F-statistic: 36.91 on 2 and 15 DF,  p-value: 1.61e-06\n"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.fit = lm(leaderboard_score ~ accuracy + logloss + AUC + f1 + mu + std, \n",
    "            data      = score_data, \n",
    "            na.action = na.omit)\n",
    "\n",
    "slm.fit = step(lm.fit, direction = \"both\")\n",
    "summary(slm.fit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot Predicted Scores vs Actual Leaderboard Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>models</th><th scope=col>scores</th><th scope=col>predictions</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>GradientBoostingClassifier </td><td>0.4452</td><td>0.4947</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>ExtraTreesClassifier       </td><td>0.4729</td><td>0.4496</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>XGBClassifier              </td><td>0.4851</td><td>0.5974</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>SGDClassifier              </td><td>0.567</td><td>0.5662</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>AdaBoostClassifier         </td><td>0.6642</td><td>0.4422</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>SVC                        </td><td>0.5336</td><td>0.6912</td></tr>\n",
       "\t<tr><th scope=row>7</th><td>BaggingClassifier          </td><td>0.4885</td><td>0.5201</td></tr>\n",
       "\t<tr><th scope=row>8</th><td>voting_ensemble_hard       </td><td>NA</td><td>0.4911</td></tr>\n",
       "\t<tr><th scope=row>9</th><td>voting_ensemble_hardWgtd   </td><td>NA</td><td>0.5016</td></tr>\n",
       "\t<tr><th scope=row>10</th><td>voting_ensemble_softWgtd   </td><td>0.4396</td><td>0.4955</td></tr>\n",
       "\t<tr><th scope=row>11</th><td>bagged_gbc                 </td><td>0.4527</td><td>0.4394</td></tr>\n",
       "\t<tr><th scope=row>12</th><td>boosted_svc                </td><td>0.5334</td><td>0.495</td></tr>\n",
       "\t<tr><th scope=row>13</th><td>LogisticRegression         </td><td>0.4411</td><td>0.4922</td></tr>\n",
       "\t<tr><th scope=row>14</th><td>LogisticRegressionCV       </td><td>0.4457</td><td>0.495</td></tr>\n",
       "\t<tr><th scope=row>15</th><td>bagged_logit               </td><td>0.4442</td><td>0.4834</td></tr>\n",
       "\t<tr><th scope=row>16</th><td>boosted_logit              </td><td>0.5891</td><td>0.4895</td></tr>\n",
       "\t<tr><th scope=row>17</th><td>scikit_nn                  </td><td>0.502</td><td>0.5763</td></tr>\n",
       "\t<tr><th scope=row>18</th><td>bagged_scikit_nn           </td><td>0.4465</td><td>0.6338</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       "  & models & scores & predictions\\\\\n",
       "\\hline\n",
       "\t1 & GradientBoostingClassifier  & 0.4452 & 0.4947\\\\\n",
       "\t2 & ExtraTreesClassifier        & 0.4729 & 0.4496\\\\\n",
       "\t3 & XGBClassifier               & 0.4851 & 0.5974\\\\\n",
       "\t4 & SGDClassifier               & 0.567 & 0.5662\\\\\n",
       "\t5 & AdaBoostClassifier          & 0.6642 & 0.4422\\\\\n",
       "\t6 & SVC                         & 0.5336 & 0.6912\\\\\n",
       "\t7 & BaggingClassifier           & 0.4885 & 0.5201\\\\\n",
       "\t8 & voting_ensemble_hard        & NA & 0.4911\\\\\n",
       "\t9 & voting_ensemble_hardWgtd    & NA & 0.5016\\\\\n",
       "\t10 & voting_ensemble_softWgtd    & 0.4396 & 0.4955\\\\\n",
       "\t11 & bagged_gbc                  & 0.4527 & 0.4394\\\\\n",
       "\t12 & boosted_svc                 & 0.5334 & 0.495\\\\\n",
       "\t13 & LogisticRegression          & 0.4411 & 0.4922\\\\\n",
       "\t14 & LogisticRegressionCV        & 0.4457 & 0.495\\\\\n",
       "\t15 & bagged_logit                & 0.4442 & 0.4834\\\\\n",
       "\t16 & boosted_logit               & 0.5891 & 0.4895\\\\\n",
       "\t17 & scikit_nn                   & 0.502 & 0.5763\\\\\n",
       "\t18 & bagged_scikit_nn            & 0.4465 & 0.6338\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "                        models scores predictions\n",
       "1  GradientBoostingClassifier  0.4452      0.4947\n",
       "2  ExtraTreesClassifier        0.4729      0.4496\n",
       "3  XGBClassifier               0.4851      0.5974\n",
       "4  SGDClassifier               0.5670      0.5662\n",
       "5  AdaBoostClassifier          0.6642      0.4422\n",
       "6  SVC                         0.5336      0.6912\n",
       "7  BaggingClassifier           0.4885      0.5201\n",
       "8  voting_ensemble_hard            NA      0.4911\n",
       "9  voting_ensemble_hardWgtd        NA      0.5016\n",
       "10 voting_ensemble_softWgtd    0.4396      0.4955\n",
       "11 bagged_gbc                  0.4527      0.4394\n",
       "12 boosted_svc                 0.5334      0.4950\n",
       "13 LogisticRegression          0.4411      0.4922\n",
       "14 LogisticRegressionCV        0.4457      0.4950\n",
       "15 bagged_logit                0.4442      0.4834\n",
       "16 boosted_logit               0.5891      0.4895\n",
       "17 scikit_nn                   0.5020      0.5763\n",
       "18 bagged_scikit_nn            0.4465      0.6338"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = c()\n",
    "models = c()\n",
    "scores = c()\n",
    "for (i in 1:nrow(score_data)) {\n",
    "    if (is.na(score_data[i,'std'])) {next}\n",
    "    if (score_data[i,'model']=='RandomForestClassifier     ') {next} # a far outlier\n",
    "    if (score_data[i,'model']=='KNeighborsClassifier       ') {next} # a far outlier\n",
    "    \n",
    "#     print(paste0(\"|\",score_data[i,'model'],\"|\"))\n",
    "    \n",
    "    \n",
    "    models = c(models, score_data[i,'model'])\n",
    "    scores = c(scores, score_data[i,'leaderboard_score'])\n",
    "    \n",
    "    accuracy = score_data[i,'accuracy']\n",
    "    logloss  = score_data[i,'logloss']\n",
    "    AUC      = score_data[i,'AUC']\n",
    "    f1       = score_data[i,'f1']\n",
    "    mu       = score_data[i,'mu']\n",
    "    std      = score_data[i,'std']\n",
    "    predictions = c(predictions, round(predict(object=slm.fit,\n",
    "                                 newdata  = data.frame(accuracy,logloss,AUC,f1,mu,std)),4))\n",
    "}\n",
    "pred_v_act = data.frame(models,scores,predictions)\n",
    "pred_v_act"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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",
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       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "Plot with title “Predicted Score v Leaderboard”"
      ]
     },
     "metadata": {
      "image/svg+xml": {
       "isolated": true
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#par(pin=c(6,6))\n",
    "library(car)\n",
    "plot(pred_v_act[,'predictions'], pred_v_act[,'scores'], main=\"Predicted Score v Leaderboard\", \n",
    "    ylab=\"Leaderboard (worse ->)\", xlab=\"Predicted Score\", pch=19)#, xlim=c(0.25,1.8),ylim=c(0.25,1.8))\n",
    "text(pred_v_act[,'predictions'], pred_v_act[,'scores'], labels=models, cex= 0.6)\n",
    "abline(coef=c(0,1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|model                      | leaderboard_score| \n",
      "|:--------------------------|-----------------:| \n",
      "|bagged_nolearn             |            0.4313| \n",
      "|ensemble of averages       |            0.4370| \n",
      "|voting_ensemble_softWgtd   |            0.4396| \n",
      "|LogisticRegression         |            0.4411| \n",
      "|bagged_logit               |            0.4442| \n",
      "|GradientBoostingClassifier |            0.4452| \n",
      "|LogisticRegressionCV       |            0.4457| \n",
      "|bagged_scikit_nn           |            0.4465| \n",
      "|bagged_gbc                 |            0.4527| \n",
      "|nolearn                    |            0.4566| \n",
      "|ExtraTreesClassifier       |            0.4729| \n",
      "|blending_ensemble          |            0.4834| \n",
      "|XGBClassifier              |            0.4851| \n",
      "|BaggingClassifier          |            0.4885| \n",
      "|scikit_nn                  |            0.5020| \n",
      "|boosted_svc                |            0.5334| \n",
      "|SVC                        |            0.5336| \n",
      "|SGDClassifier              |            0.5670| \n",
      "|cosine_similarity          |            0.5732| \n",
      "|boosted_logit              |            0.5891| \n",
      "|KMeans                     |            0.6289| \n",
      "|AdaBoostClassifier         |            0.6642| \n",
      "|KNeighborsClassifier       |            1.1870| \n",
      "|RandomForestClassifier     |            1.7907| \n",
      "|voting_ensemble_hard       |                NA| \n",
      "|voting_ensemble_hardWgtd   |                NA| \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "NULL"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_data = score_data[with(score_data, order(leaderboard_score)), ]\n",
    "\n",
    "library(knitr)\n",
    "foo = kable(score_data, format = \"markdown\", digits = 4)\n",
    "foof = ''\n",
    "for (i in 1:length(foo)) {\n",
    "    subs = substr(foo[i],5,52)\n",
    "    foof = cat(foof,cat(subs,'\\n'))\n",
    "}\n",
    "foof"
   ]
  },
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