demo-plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WindowsPath('C:/Users/hlao1/Github/kaggle-titanic')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "WindowsPath('C:/Users/hlao1/Github/kaggle-titanic/input')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "['gender_submission.csv', 'test.csv', 'train.csv']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# std python utility\n",
    "from pathlib import Path\n",
    "from collections import Counter\n",
    "import os\n",
    "# ds utility\n",
    "import pandas\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "# ds modelling\n",
    "# import sklearn\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(action=\"ignore\")\n",
    "pd.options.display.max_seq_items = 8000\n",
    "pd.options.display.max_rows = 8000\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "# directory setup\n",
    "ROOT = Path(os.getcwd())\n",
    "DATA_DIR = ROOT / \"input\"\n",
    "PLOT_DIR = ROOT / \"plots\"\n",
    "\n",
    "display(ROOT, DATA_DIR)\n",
    "os.listdir(DATA_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>SibSp</th>\n",
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       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>A/5 21171</td>\n",
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       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
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     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    },
    {
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       "None"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Train shape:'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Test shappe:'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(418, 11)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "['PassengerId',\n",
       " 'Survived',\n",
       " 'Pclass',\n",
       " 'Name',\n",
       " 'Sex',\n",
       " 'Age',\n",
       " 'SibSp',\n",
       " 'Parch',\n",
       " 'Ticket',\n",
       " 'Fare',\n",
       " 'Cabin',\n",
       " 'Embarked']"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of features: 12\n"
     ]
    }
   ],
   "source": [
    "# Load datasets\n",
    "train = pd.read_csv(DATA_DIR/\"train.csv\")\n",
    "test = pd.read_csv(DATA_DIR/\"test.csv\")\n",
    "train_shape = train.shape\n",
    "test_shape = test.shape\n",
    "feat_cols = train.columns.to_list()\n",
    "# surface level inspection of the training data loaded\n",
    "display(train.head())\n",
    "# display general info about the dataset\n",
    "display(train.info())\n",
    "# observe general stats of feat-cols \n",
    "display(train.describe())\n",
    "display(\"Train shape:\",train_shape)\n",
    "display(\"Test shappe:\",test_shape)\n",
    "display(feat_cols)\n",
    "print(\"Number of features:\",len(feat_cols))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exclude PassengerId because it is known to be a a unique identifier -- an index of the instance defined. 11 features of interest.\n",
    "\n",
    "- How do the various features relate to one another? Ex. Sex vs Survived?\n",
    "- Is there missing data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.257307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.338481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.549500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.096067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>0.159651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Survived    Pclass       Age     SibSp     Parch      Fare\n",
       "Survived  1.000000 -0.338481 -0.077221 -0.035322  0.081629  0.257307\n",
       "Pclass   -0.338481  1.000000 -0.369226  0.083081  0.018443 -0.549500\n",
       "Age      -0.077221 -0.369226  1.000000 -0.308247 -0.189119  0.096067\n",
       "SibSp    -0.035322  0.083081 -0.308247  1.000000  0.414838  0.159651\n",
       "Parch     0.081629  0.018443 -0.189119  0.414838  1.000000  0.216225"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['Survived','Pclass','Name','Sex','Age','SibSp','Parch','Ticket','Fare','Cabin','Embarked']\n",
    "corr = train[cols].corr()\n",
    "corr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(15,12))\n",
    "sns_plot = sns.heatmap(corr, vmax=0.9, cmap=\"coolwarm\", square=True, annot= True)\n",
    "fig = sns_plot.get_figure()\n",
    "fig.savefig(PLOT_DIR/\"train_heatmap.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fare    Pclass     -0.549500\n",
      "Age     Pclass     -0.369226\n",
      "Pclass  Survived   -0.338481\n",
      "SibSp   Age        -0.308247\n",
      "Parch   Age        -0.189119\n",
      "Age     Survived   -0.077221\n",
      "SibSp   Survived   -0.035322\n",
      "Parch   Pclass      0.018443\n",
      "        Survived    0.081629\n",
      "SibSp   Pclass      0.083081\n",
      "Fare    Age         0.096067\n",
      "        SibSp       0.159651\n",
      "        Parch       0.216225\n",
      "        Survived    0.257307\n",
      "Parch   SibSp       0.414838\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# sorted_mat = corr.unstack().sort_values() # includes duplications\n",
    "\n",
    "# Retain upper triangular values of correlation matrix and\n",
    "# make Lower triangular values Null\n",
    "upper_corr_mat = corr.where(\n",
    "    np.triu(np.ones(corr.shape), k=1).astype(np.bool))\n",
    "  \n",
    "# Convert to 1-D series and drop Null values\n",
    "unique_corr_pairs = upper_corr_mat.unstack().dropna()\n",
    "  \n",
    "# Sort correlation pairs\n",
    "sorted_mat = unique_corr_pairs.sort_values()\n",
    "print(sorted_mat)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# classification_setup = classification.setup(data=train, target=\"Survived\")"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# Missing Features in Training Dataset\n",
    "all_cols = train.isna().any()\n",
    "na_cols = [c for c in all_cols.index if all_cols[c] == True]\n",
    "display(\"All columns:\",train.isna().any(), \"Missing cols:\", na_cols)\n",
    "print()\n",
    "for c in na_cols:\n",
    "    na_count = train[c].isna().sum()\n",
    "    total = len(train[c])\n",
    "    na_percent =  na_count / total * 100\n",
    "    print(\"{:<10}: {:<5} / {:<5} instances missing --> {:1.5f}%\".format(c, na_count, total, na_percent))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Model\t| Accuracy\t|AUC\t|Recall\t|Prec.\t|F1\t|Kappa\t|MCC\t|TT (Sec)|\n",
    "|---|---|---|---|---|---|---|---|---|\n",
    "|ridge\t|Ridge Classifier\t|0.8204\t|0.0000\t|0.7348\t|0.8106\t|0.7672\t|0.6219\t|0.6275\t|0.0340|\n",
    "|et\t|Extra Trees Classifier\t|0.8171\t|0.8505\t|0.6792\t|0.8420\t|0.7481\t|0.6078\t|0.6196\t|0.1810|\n",
    "|catboost|\tCatBoost Classifier\t|0.8170\t|0.8716\t|0.6472\t|0.8722\t|0.7381\t|0.6033\t|0.6228\t|3.5190|\n",
    "|lr\t|Logistic Regression\t|0.8108\t|0.8606\t|0.7348\t|0.7900\t|0.7570\t|0.6029\t|0.6082\t|1.3130|\n",
    "|gbc\t|Gradient Boosting Classifier\t|0.8074\t|0.8540\t|0.6391\t|0.8508\t|0.7241\t|0.5823\t|0.6004\t|0.2360\n",
    "|rf\t|Random Forest Classifier\t|0.8058\t|0.8586\t|0.6592\t|0.8296\t|0.7302\t|0.5826\t|0.5956\t|0.2010|\n",
    "|dt\t|Decision Tree Classifier\t|0.8028\t|0.7907\t|0.7269\t|0.7766\t|0.7489\t|0.5870\t|0.5900\t|0.0330|\n",
    "|ada\t|Ada Boost Classifier\t|0.8009\t|0.8550\t|0.6985\t|0.7886\t|0.7348\t|0.5778\t|0.5849\t|0.1100|\n",
    "|xgboost|\tExtreme Gradient Boosting\t|0.7818\t|0.8530\t|0.7148\t|0.7396\t|0.7222\t|0.5435\t|0.5482\t|0.8680|\n",
    "|lightgbm|\tLight Gradient Boosting Machine\t|0.7817\t|0.8653\t|0.6871\t|0.7585\t|0.7154\t|0.5400\t|0.5468\t|0.1530|\n",
    "|knn\t|K Neighbors Classifier\t|0.6903\t|0.7146\t|0.5398\t|0.6461\t|0.5839\t|0.3413\t|0.3474\t|0.0700|\n",
    "|svm\t|SVM - Linear Kernel\t|0.6228\t|0.0000\t|0.7485\t|0.6053\t|0.6163\t|0.2840\t|0.3131\t|0.0420|\n",
    "|lda\t|Linear Discriminant Analysis\t|0.6081\t|0.6264\t|0.4848\t|0.5380\t|0.5038\t|0.1838\t|0.1847\t|0.1940|\n",
    "|nb\t|Naive Bayes\t|0.4751\t|0.5516\t|0.9522\t|0.4330\t|0.5950\t|0.0871\t|0.1611\t|0.0320|\n",
    "|qda\t|Quadratic Discriminant Analysis\t|0.4045\t|0.5000\t|1.0000\t|0.4045\t|0.5760\t|0.0000\t|0.0000\t|0.0750|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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