Caret Modeling Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing Different Classification Models with Caret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Attaching package: ‘dplyr’\n",
      "\n",
      "The following objects are masked from ‘package:plyr’:\n",
      "\n",
      "    arrange, count, desc, failwith, id, mutate, rename, summarise,\n",
      "    summarize\n",
      "\n",
      "The following object is masked from ‘package:randomForest’:\n",
      "\n",
      "    combine\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(plyr)\n",
    "library(dplyr)\n",
    "library(ggplot2)\n",
    "library(caret)\n",
    "# library(RANN)\n",
    "library(doMC)\n",
    "registerDoMC(cores = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t889 obs. of  11 variables:\n",
      " $ PassengerId: int  1 2 3 4 5 6 7 8 9 10 ...\n",
      " $ Survived   : int  0 1 1 1 0 0 0 0 1 1 ...\n",
      " $ Pclass     : int  3 1 3 1 3 3 1 3 3 2 ...\n",
      " $ Name       : Factor w/ 889 levels \"Abbing, Mr. Anthony\",..: 109 191 358 277 16 558 519 628 416 580 ...\n",
      " $ Sex        : Factor w/ 2 levels \"female\",\"male\": 2 1 1 1 2 2 2 2 1 1 ...\n",
      " $ Age        : num  22 38 26 35 35 ...\n",
      " $ SibSp      : int  1 1 0 1 0 0 0 3 0 1 ...\n",
      " $ Parch      : int  0 0 0 0 0 0 0 1 2 0 ...\n",
      " $ Ticket     : Factor w/ 680 levels \"110152\",\"110413\",..: 523 596 669 49 472 275 85 395 344 132 ...\n",
      " $ Fare       : num  7.25 71.28 7.92 53.1 8.05 ...\n",
      " $ Embarked   : Factor w/ 3 levels \"C\",\"Q\",\"S\": 3 1 3 3 3 2 3 3 3 1 ...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "0"
      ],
      "text/latex": [
       "0"
      ],
      "text/markdown": [
       "0"
      ],
      "text/plain": [
       "[1] 0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train <- read.csv('datasets/train_edited.csv', stringsAsFactors = TRUE)\n",
    "str(train)\n",
    "sum(is.na(train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#get rid of useless features\n",
    "train <- train %>% select(-PassengerId, -Name, -Ticket)\n",
    "train_survived <- train$Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Survived</th><th scope=col>Pclass</th><th scope=col>Sex</th><th scope=col>Age</th><th scope=col>SibSp</th><th scope=col>Parch</th><th scope=col>Fare</th><th scope=col>Embarked</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>0           </td><td> 0.8247444  </td><td>male        </td><td>-0.590162723</td><td> 0.4311076  </td><td>-0.474059   </td><td>-0.4999583  </td><td>S           </td></tr>\n",
       "\t<tr><td>1           </td><td>-1.5713267  </td><td>female      </td><td> 0.643608719</td><td> 0.4311076  </td><td>-0.474059   </td><td> 0.7885028  </td><td>C           </td></tr>\n",
       "\t<tr><td>1           </td><td> 0.8247444  </td><td>female      </td><td>-0.281719862</td><td>-0.4749317  </td><td>-0.474059   </td><td>-0.4863762  </td><td>S           </td></tr>\n",
       "\t<tr><td>1           </td><td>-1.5713267  </td><td>female      </td><td> 0.412276574</td><td> 0.4311076  </td><td>-0.474059   </td><td> 0.4226232  </td><td>S           </td></tr>\n",
       "\t<tr><td>0           </td><td> 0.8247444  </td><td>male        </td><td> 0.412276574</td><td>-0.4749317  </td><td>-0.474059   </td><td>-0.4838609  </td><td>S           </td></tr>\n",
       "\t<tr><td>0           </td><td> 0.8247444  </td><td>male        </td><td> 0.003521745</td><td>-0.4749317  </td><td>-0.474059   </td><td>-0.4756452  </td><td>Q           </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllll}\n",
       " Survived & Pclass & Sex & Age & SibSp & Parch & Fare & Embarked\\\\\n",
       "\\hline\n",
       "\t 0            &  0.8247444   & male         & -0.590162723 &  0.4311076   & -0.474059    & -0.4999583   & S           \\\\\n",
       "\t 1            & -1.5713267   & female       &  0.643608719 &  0.4311076   & -0.474059    &  0.7885028   & C           \\\\\n",
       "\t 1            &  0.8247444   & female       & -0.281719862 & -0.4749317   & -0.474059    & -0.4863762   & S           \\\\\n",
       "\t 1            & -1.5713267   & female       &  0.412276574 &  0.4311076   & -0.474059    &  0.4226232   & S           \\\\\n",
       "\t 0            &  0.8247444   & male         &  0.412276574 & -0.4749317   & -0.474059    & -0.4838609   & S           \\\\\n",
       "\t 0            &  0.8247444   & male         &  0.003521745 & -0.4749317   & -0.474059    & -0.4756452   & Q           \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "Survived | Pclass | Sex | Age | SibSp | Parch | Fare | Embarked | \n",
       "|---|---|---|---|---|---|\n",
       "| 0            |  0.8247444   | male         | -0.590162723 |  0.4311076   | -0.474059    | -0.4999583   | S            | \n",
       "| 1            | -1.5713267   | female       |  0.643608719 |  0.4311076   | -0.474059    |  0.7885028   | C            | \n",
       "| 1            |  0.8247444   | female       | -0.281719862 | -0.4749317   | -0.474059    | -0.4863762   | S            | \n",
       "| 1            | -1.5713267   | female       |  0.412276574 |  0.4311076   | -0.474059    |  0.4226232   | S            | \n",
       "| 0            |  0.8247444   | male         |  0.412276574 | -0.4749317   | -0.474059    | -0.4838609   | S            | \n",
       "| 0            |  0.8247444   | male         |  0.003521745 | -0.4749317   | -0.474059    | -0.4756452   | Q            | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  Survived Pclass     Sex    Age          SibSp      Parch     Fare      \n",
       "1 0         0.8247444 male   -0.590162723  0.4311076 -0.474059 -0.4999583\n",
       "2 1        -1.5713267 female  0.643608719  0.4311076 -0.474059  0.7885028\n",
       "3 1         0.8247444 female -0.281719862 -0.4749317 -0.474059 -0.4863762\n",
       "4 1        -1.5713267 female  0.412276574  0.4311076 -0.474059  0.4226232\n",
       "5 0         0.8247444 male    0.412276574 -0.4749317 -0.474059 -0.4838609\n",
       "6 0         0.8247444 male    0.003521745 -0.4749317 -0.474059 -0.4756452\n",
       "  Embarked\n",
       "1 S       \n",
       "2 C       \n",
       "3 S       \n",
       "4 S       \n",
       "5 S       \n",
       "6 Q       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preProcValues <- preProcess(train, method = c(\"center\", \"scale\"))\n",
    "train_processed <- predict(preProcValues, train)\n",
    "train_processed$Survived <- train_survived\n",
    "head(train_processed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t889 obs. of  9 variables:\n",
      " $ Survived  : num  0 1 1 1 0 0 0 0 1 1 ...\n",
      " $ Pclass    : num  0.825 -1.571 0.825 -1.571 0.825 ...\n",
      " $ Sex.male  : num  1 0 0 0 1 1 1 1 0 0 ...\n",
      " $ Age       : num  -0.59 0.644 -0.282 0.412 0.412 ...\n",
      " $ SibSp     : num  0.431 0.431 -0.475 0.431 -0.475 ...\n",
      " $ Parch     : num  -0.474 -0.474 -0.474 -0.474 -0.474 ...\n",
      " $ Fare      : num  -0.5 0.789 -0.486 0.423 -0.484 ...\n",
      " $ Embarked.Q: num  0 0 0 0 0 1 0 0 0 0 ...\n",
      " $ Embarked.S: num  1 0 1 1 1 0 1 1 1 0 ...\n"
     ]
    }
   ],
   "source": [
    "dmy <- dummyVars(\"~ .\", data = train_processed, fullRank = TRUE)\n",
    "train_transformed <- data.frame(predict(dmy, newdata = train_processed))\n",
    "str(train_transformed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#not sure if we need to convert survived into a factor - might as well but experiment\n",
    "train_transformed$Survived <- as.factor(train_transformed$Survived)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t667 obs. of  9 variables:\n",
      " $ Survived  : Factor w/ 2 levels \"0\",\"1\": 1 2 2 1 1 1 1 2 1 1 ...\n",
      " $ Pclass    : num  0.825 -1.571 -1.571 0.825 0.825 ...\n",
      " $ Sex.male  : num  1 0 0 1 1 1 1 0 1 1 ...\n",
      " $ Age       : num  -0.59016 0.64361 0.41228 0.41228 0.00352 ...\n",
      " $ SibSp     : num  0.431 0.431 0.431 -0.475 -0.475 ...\n",
      " $ Parch     : num  -0.474 -0.474 -0.474 -0.474 -0.474 ...\n",
      " $ Fare      : num  -0.5 0.789 0.423 -0.484 -0.476 ...\n",
      " $ Embarked.Q: num  0 0 0 0 1 0 0 0 0 0 ...\n",
      " $ Embarked.S: num  1 0 1 1 0 1 1 1 1 1 ...\n"
     ]
    }
   ],
   "source": [
    "index <- createDataPartition(train_transformed$Survived, p = 0.75, list = FALSE)\n",
    "trainSet <- train_transformed[index, ]\n",
    "testSet <- train_transformed[-index, ]\n",
    "\n",
    "str(trainSet)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Possibly look into feature selection later on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictors<-c(\"Pclass\", \"Sex.male\", \"Age\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked.Q\", \"Embarked.S\")\n",
    "outcomeName <- \"Survived\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter   TrainDeviance   ValidDeviance   StepSize   Improve\n",
      "     1        1.2573             nan     0.1000    0.0371\n",
      "     2        1.1971             nan     0.1000    0.0294\n",
      "     3        1.1468             nan     0.1000    0.0233\n",
      "     4        1.1045             nan     0.1000    0.0203\n",
      "     5        1.0706             nan     0.1000    0.0164\n",
      "     6        1.0439             nan     0.1000    0.0101\n",
      "     7        1.0151             nan     0.1000    0.0128\n",
      "     8        0.9930             nan     0.1000    0.0094\n",
      "     9        0.9770             nan     0.1000    0.0061\n",
      "    10        0.9584             nan     0.1000    0.0061\n",
      "    20        0.8508             nan     0.1000    0.0005\n",
      "    40        0.7648             nan     0.1000   -0.0019\n",
      "    60        0.7221             nan     0.1000   -0.0009\n",
      "    80        0.6880             nan     0.1000   -0.0016\n",
      "   100        0.6599             nan     0.1000   -0.0004\n",
      "   120        0.6414             nan     0.1000    0.0006\n",
      "   140        0.6206             nan     0.1000   -0.0016\n",
      "   150        0.6127             nan     0.1000   -0.0009\n",
      "\n",
      "# weights:  31\n",
      "initial  value 456.124994 \n",
      "iter  10 value 297.457540\n",
      "iter  20 value 278.865299\n",
      "iter  30 value 272.268308\n",
      "iter  40 value 269.704808\n",
      "iter  50 value 268.962167\n",
      "iter  60 value 268.913371\n",
      "iter  70 value 268.910158\n",
      "final  value 268.910135 \n",
      "converged\n"
     ]
    }
   ],
   "source": [
    "fitControl <- trainControl(method = 'repeatedcv', number = 5, repeats = 5)\n",
    "model_rf <- train(trainSet[, predictors], trainSet[, outcomeName], method = 'rf', trControl = fitControl)\n",
    "model_glm <- train(trainSet[, predictors], trainSet[, outcomeName], method = 'glm', family = binomial(link = 'logit'), trControl = fitControl)\n",
    "model_gbm <- train(trainSet[, predictors], trainSet[, outcomeName], method = 'gbm', trControl = fitControl)\n",
    "model_nnet <- train(trainSet[, predictors], trainSet[, outcomeName], method = 'nnet', trControl = fitControl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rf variable importance\n",
       "\n",
       "           Overall\n",
       "Sex.male   100.000\n",
       "Fare        68.854\n",
       "Age         51.774\n",
       "Pclass      38.613\n",
       "SibSp       14.312\n",
       "Parch       12.136\n",
       "Embarked.S   4.831\n",
       "Embarked.Q   0.000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "varImp(object = model_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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598cuSPqbsEfv3rX8fpp5/eXZVSm/jFL34Rv/vd70h0mcAxxxwT5557bpfVSnWO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3yX5HPT7bff7h+j4YKkQI/U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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(varImp(object = model_rf), main=\"RF - Variable Importance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "predictions_rf\n",
       "  0   1 \n",
       "155  67 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions_rf <- predict.train(object = model_rf, testSet[, predictors], type = 'raw')\n",
    "table(predictions_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Confusion Matrix and Statistics\n",
       "\n",
       "          Reference\n",
       "Prediction   0   1\n",
       "         0 128  27\n",
       "         1   9  58\n",
       "                                          \n",
       "               Accuracy : 0.8378          \n",
       "                 95% CI : (0.7826, 0.8838)\n",
       "    No Information Rate : 0.6171          \n",
       "    P-Value [Acc > NIR] : 5.643e-13       \n",
       "                                          \n",
       "                  Kappa : 0.6425          \n",
       " Mcnemar's Test P-Value : 0.004607        \n",
       "                                          \n",
       "            Sensitivity : 0.9343          \n",
       "            Specificity : 0.6824          \n",
       "         Pos Pred Value : 0.8258          \n",
       "         Neg Pred Value : 0.8657          \n",
       "             Prevalence : 0.6171          \n",
       "         Detection Rate : 0.5766          \n",
       "   Detection Prevalence : 0.6982          \n",
       "      Balanced Accuracy : 0.8083          \n",
       "                                          \n",
       "       'Positive' Class : 0               \n",
       "                                          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusionMatrix(predictions_rf, testSet[, outcomeName])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## General Linear Model - Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "glm variable importance\n",
       "\n",
       "            Overall\n",
       "Sex.male   100.0000\n",
       "Pclass      60.7254\n",
       "Age         39.3666\n",
       "SibSp       17.6167\n",
       "Fare         6.6408\n",
       "Embarked.S   5.3621\n",
       "Embarked.Q   0.3959\n",
       "Parch        0.0000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "varImp(object = model_glm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CBAgAABAgQGQUCANAjIbkGAAAECBAgQIECAQGsICJBao52UkgABAgQIECBAgACB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rpXfffTe9/PLL+Vj8E71XcTzm9cw+++z5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\nQKCvAv8fEcx6e95DwRYAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(varImp(object = model_glm), main=\"GLM - Variable Importance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "predictions_glm\n",
       "  0   1 \n",
       "130  92 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions_glm <- predict.train(object = model_glm, testSet[, predictors], type = 'raw')\n",
    "table(predictions_glm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Confusion Matrix and Statistics\n",
       "\n",
       "          Reference\n",
       "Prediction   0   1\n",
       "         0 110  20\n",
       "         1  27  65\n",
       "                                          \n",
       "               Accuracy : 0.7883          \n",
       "                 95% CI : (0.7286, 0.8401)\n",
       "    No Information Rate : 0.6171          \n",
       "    P-Value [Acc > NIR] : 3.488e-08       \n",
       "                                          \n",
       "                  Kappa : 0.5589          \n",
       " Mcnemar's Test P-Value : 0.3815          \n",
       "                                          \n",
       "            Sensitivity : 0.8029          \n",
       "            Specificity : 0.7647          \n",
       "         Pos Pred Value : 0.8462          \n",
       "         Neg Pred Value : 0.7065          \n",
       "             Prevalence : 0.6171          \n",
       "         Detection Rate : 0.4955          \n",
       "   Detection Prevalence : 0.5856          \n",
       "      Balanced Accuracy : 0.7838          \n",
       "                                          \n",
       "       'Positive' Class : 0               \n",
       "                                          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusionMatrix(predictions_glm, testSet[, outcomeName])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GBM - Generalized Boosted Regression Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gbm variable importance\n",
       "\n",
       "           Overall\n",
       "Sex.male   100.000\n",
       "Fare        68.722\n",
       "Age         65.971\n",
       "Pclass      43.552\n",
       "SibSp       12.267\n",
       "Parch        7.958\n",
       "Embarked.S   7.931\n",
       "Embarked.Q   0.000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "varImp(object = model_gbm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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G6IoN2GAAECBAgQIECAAIHhLyBAGv5tpIQECBAgQIAAAQIECAyRgABpiKDdhgAB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c1KOGzYMN+SdP/99/uyU6+x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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(varImp(object = model_gbm), main=\"GBM - Variable Importance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "predictions_gbm\n",
       "  0   1 \n",
       "145  77 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions_gbm <- predict.train(object = model_gbm, testSet[, predictors], type = 'raw')\n",
    "table(predictions_gbm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Confusion Matrix and Statistics\n",
       "\n",
       "          Reference\n",
       "Prediction   0   1\n",
       "         0 123  22\n",
       "         1  14  63\n",
       "                                          \n",
       "               Accuracy : 0.8378          \n",
       "                 95% CI : (0.7826, 0.8838)\n",
       "    No Information Rate : 0.6171          \n",
       "    P-Value [Acc > NIR] : 5.643e-13       \n",
       "                                          \n",
       "                  Kappa : 0.6506          \n",
       " Mcnemar's Test P-Value : 0.2433          \n",
       "                                          \n",
       "            Sensitivity : 0.8978          \n",
       "            Specificity : 0.7412          \n",
       "         Pos Pred Value : 0.8483          \n",
       "         Neg Pred Value : 0.8182          \n",
       "             Prevalence : 0.6171          \n",
       "         Detection Rate : 0.5541          \n",
       "   Detection Prevalence : 0.6532          \n",
       "      Balanced Accuracy : 0.8195          \n",
       "                                          \n",
       "       'Positive' Class : 0               \n",
       "                                          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusionMatrix(predictions_gbm, testSet[, outcomeName])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nnet variable importance\n",
       "\n",
       "           Overall\n",
       "Sex.male   100.000\n",
       "Parch       64.227\n",
       "SibSp       50.392\n",
       "Age         44.541\n",
       "Fare        34.070\n",
       "Embarked.Q  26.918\n",
       "Pclass       6.221\n",
       "Embarked.S   0.000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "varImp(object = model_nnet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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42pMUDKc3L5+2LFuK5iMZGuYmhltwCg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ABAYVw7e6vkFmgBRwahUjpqaO+5Fi2FxX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\nBzjZ+r5P9RylAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(varImp(object = model_nnet), main=\"NNET - Variable Importance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "predictions_nnet\n",
       "  0   1 \n",
       "150  72 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions_nnet <- predict.train(object = model_nnet, testSet[, predictors], type = 'raw')\n",
    "table(predictions_nnet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Confusion Matrix and Statistics\n",
       "\n",
       "          Reference\n",
       "Prediction   0   1\n",
       "         0 122  28\n",
       "         1  15  57\n",
       "                                          \n",
       "               Accuracy : 0.8063          \n",
       "                 95% CI : (0.7481, 0.8561)\n",
       "    No Information Rate : 0.6171          \n",
       "    P-Value [Acc > NIR] : 9.436e-10       \n",
       "                                          \n",
       "                  Kappa : 0.5779          \n",
       " Mcnemar's Test P-Value : 0.06725         \n",
       "                                          \n",
       "            Sensitivity : 0.8905          \n",
       "            Specificity : 0.6706          \n",
       "         Pos Pred Value : 0.8133          \n",
       "         Neg Pred Value : 0.7917          \n",
       "             Prevalence : 0.6171          \n",
       "         Detection Rate : 0.5495          \n",
       "   Detection Prevalence : 0.6757          \n",
       "      Balanced Accuracy : 0.7805          \n",
       "                                          \n",
       "       'Positive' Class : 0               \n",
       "                                          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusionMatrix(predictions_nnet, testSet[, outcomeName])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.3.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}