model_selection/imbalanced/imbalanced_metrics.ipynb

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    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Evaluating-Imbalanced-Datasets\" data-toc-modified-id=\"Evaluating-Imbalanced-Datasets-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Evaluating Imbalanced Datasets</a></span><ul class=\"toc-item\"><li><span><a href=\"#Dataset\" data-toc-modified-id=\"Dataset-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Dataset</a></span></li><li><span><a href=\"#Class-Weighting\" data-toc-modified-id=\"Class-Weighting-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Class Weighting</a></span></li><li><span><a href=\"#F1-Score\" data-toc-modified-id=\"F1-Score-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>F1 Score</a></span></li><li><span><a href=\"#Conclusion\" data-toc-modified-id=\"Conclusion-1.4\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>Conclusion</a></span></li></ul></li><li><span><a href=\"#Reference\" data-toc-modified-id=\"Reference-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Reference</a></span></li></ul></div>"
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   "source": [
    "# code for loading the format for the notebook\n",
    "import os\n",
    "\n",
    "# path : store the current path to convert back to it later\n",
    "path = os.getcwd()\n",
    "os.chdir(os.path.join('..', '..', 'notebook_format'))\n",
    "\n",
    "from formats import load_style\n",
    "load_style(plot_style=False)"
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
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     "text": [
      "Ethen 2019-12-16 11:30:05 \n",
      "\n",
      "CPython 3.6.4\n",
      "IPython 7.9.0\n",
      "\n",
      "numpy 1.16.5\n",
      "pandas 0.25.0\n",
      "sklearn 0.21.2\n",
      "matplotlib 3.1.1\n"
     ]
    }
   ],
   "source": [
    "os.chdir(path)\n",
    "\n",
    "# 1. magic for inline plot\n",
    "# 2. magic to print version\n",
    "# 3. magic so that the notebook will reload external python modules\n",
    "# 4. magic to enable retina (high resolution) plots\n",
    "# https://gist.github.com/minrk/3301035\n",
    "%matplotlib inline\n",
    "%load_ext watermark\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%config InlineBackend.figure_format='retina'\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import precision_recall_curve, roc_curve\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "\n",
    "%watermark -a 'Ethen' -d -t -v -p numpy,pandas,sklearn,matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluating Imbalanced Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This documentation illustrates the trade off between True Positive Rate and False Positive Rate using ROC and Precision/Recall (PR) curves. In the end, we will take a look at why, for binary classification problem, apart from solely using the popular evaluation metric ROC curve we should also look at other evaluation metric such as precision and recall especially when working with highly imbalanced dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset we'll be using today can be downloaded from the [Kaggle website](https://www.kaggle.com/mlg-ulb/creditcardfraud)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "dimension:  (284807, 31)\n"
     ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
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      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filepath = os.path.join('data', 'creditcard.csv')\n",
    "df = pd.read_csv(filepath)\n",
    "print('dimension: ', df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A brief description of the dataset based on the data overview section from the download source.\n",
    "\n",
    "> The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions.\n",
    "> Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, ... V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction amount. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.\n",
    "\n",
    "The only feature-engineering that we'll be doing for now is to convert the feature \"Time\" (seconds from which the very first data observation took place) to hours of a day. While we're at it, let's take a look at a breakdown of our legit vs fraud transactions via a pivot table and a plot of fraud transactions' count over time for a quick exploratory data analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "Class      0   1\n",
       "hour            \n",
       "0.0    10919  21\n",
       "1.0     7687   6\n",
       "2.0     4212  10\n",
       "3.0     3269  57\n",
       "4.0     3476  17\n",
       "5.0     2185  23\n",
       "6.0     2979  11\n",
       "7.0     4093   9\n",
       "8.0     7219  23\n",
       "9.0    10266   9\n",
       "10.0   15824  16\n",
       "11.0   16593   8\n",
       "12.0   16804  53\n",
       "13.0   15400  17\n",
       "14.0   15350  17\n",
       "15.0   16545  23\n",
       "16.0   16434  26\n",
       "17.0   16435  22\n",
       "18.0   16135  29\n",
       "19.0   17003  33\n",
       "20.0   15632  19\n",
       "21.0   16739  18\n",
       "22.0   17692  16\n",
       "23.0   15424   9"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['hour'] = np.ceil(df['Time'].values / 3600) % 24\n",
    "fraud_over_hour = df.pivot_table(values='Amount', index='hour', columns='Class', aggfunc='count')\n",
    "fraud_over_hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 392,
       "width": 499
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.size'] = 12\n",
    "plt.rcParams['figure.figsize'] = 8, 6\n",
    "\n",
    "\n",
    "plt.plot(fraud_over_hour[1])\n",
    "plt.title('Fraudulent Transaction over Hour')\n",
    "plt.ylabel('Fraudulent Count')\n",
    "plt.xlabel('Hour')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training data dimension: (185124, 30)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hour</th>\n",
       "      <th>Amount</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>...</th>\n",
       "      <th>V19</th>\n",
       "      <th>V20</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>183716</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.76</td>\n",
       "      <td>2.089242</td>\n",
       "      <td>0.629632</td>\n",
       "      <td>-3.364200</td>\n",
       "      <td>0.489395</td>\n",
       "      <td>1.553483</td>\n",
       "      <td>-0.986382</td>\n",
       "      <td>0.698731</td>\n",
       "      <td>-0.284807</td>\n",
       "      <td>...</td>\n",
       "      <td>0.084549</td>\n",
       "      <td>-0.107484</td>\n",
       "      <td>-0.041904</td>\n",
       "      <td>0.058986</td>\n",
       "      <td>-0.103786</td>\n",
       "      <td>0.050964</td>\n",
       "      <td>0.416364</td>\n",
       "      <td>0.694420</td>\n",
       "      <td>-0.082845</td>\n",
       "      <td>-0.028126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145898</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.49</td>\n",
       "      <td>-0.914450</td>\n",
       "      <td>2.361973</td>\n",
       "      <td>0.335526</td>\n",
       "      <td>4.114377</td>\n",
       "      <td>0.557196</td>\n",
       "      <td>0.874183</td>\n",
       "      <td>0.332492</td>\n",
       "      <td>0.540016</td>\n",
       "      <td>...</td>\n",
       "      <td>1.614699</td>\n",
       "      <td>0.352808</td>\n",
       "      <td>-0.159933</td>\n",
       "      <td>-0.261357</td>\n",
       "      <td>0.186308</td>\n",
       "      <td>0.718244</td>\n",
       "      <td>-0.911284</td>\n",
       "      <td>-0.093219</td>\n",
       "      <td>-0.282263</td>\n",
       "      <td>-0.229368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247003</th>\n",
       "      <td>19.0</td>\n",
       "      <td>2.69</td>\n",
       "      <td>2.070918</td>\n",
       "      <td>-0.062049</td>\n",
       "      <td>-1.128898</td>\n",
       "      <td>0.383220</td>\n",
       "      <td>-0.079241</td>\n",
       "      <td>-1.130912</td>\n",
       "      <td>0.177956</td>\n",
       "      <td>-0.321792</td>\n",
       "      <td>...</td>\n",
       "      <td>0.201919</td>\n",
       "      <td>-0.222333</td>\n",
       "      <td>-0.299237</td>\n",
       "      <td>-0.732516</td>\n",
       "      <td>0.322038</td>\n",
       "      <td>-0.112259</td>\n",
       "      <td>-0.281414</td>\n",
       "      <td>0.205225</td>\n",
       "      <td>-0.071557</td>\n",
       "      <td>-0.061429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56024</th>\n",
       "      <td>14.0</td>\n",
       "      <td>159.95</td>\n",
       "      <td>0.929003</td>\n",
       "      <td>-0.262584</td>\n",
       "      <td>-0.026089</td>\n",
       "      <td>0.596927</td>\n",
       "      <td>-0.469012</td>\n",
       "      <td>-1.022218</td>\n",
       "      <td>0.463968</td>\n",
       "      <td>-0.176288</td>\n",
       "      <td>...</td>\n",
       "      <td>0.278433</td>\n",
       "      <td>0.152181</td>\n",
       "      <td>-0.236715</td>\n",
       "      <td>-1.149710</td>\n",
       "      <td>0.035834</td>\n",
       "      <td>0.504788</td>\n",
       "      <td>0.139781</td>\n",
       "      <td>0.095334</td>\n",
       "      <td>-0.102652</td>\n",
       "      <td>0.025986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217153</th>\n",
       "      <td>16.0</td>\n",
       "      <td>44.22</td>\n",
       "      <td>1.831384</td>\n",
       "      <td>-0.814800</td>\n",
       "      <td>-0.727692</td>\n",
       "      <td>-0.075094</td>\n",
       "      <td>0.028202</td>\n",
       "      <td>1.380668</td>\n",
       "      <td>-0.941283</td>\n",
       "      <td>0.502667</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.051171</td>\n",
       "      <td>-0.240155</td>\n",
       "      <td>0.242974</td>\n",
       "      <td>0.905053</td>\n",
       "      <td>0.189693</td>\n",
       "      <td>-0.540499</td>\n",
       "      <td>-0.358400</td>\n",
       "      <td>0.202675</td>\n",
       "      <td>0.048368</td>\n",
       "      <td>-0.044683</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        hour  Amount        V1        V2        V3        V4        V5  \\\n",
       "183716  11.0    0.76  2.089242  0.629632 -3.364200  0.489395  1.553483   \n",
       "145898   1.0    0.49 -0.914450  2.361973  0.335526  4.114377  0.557196   \n",
       "247003  19.0    2.69  2.070918 -0.062049 -1.128898  0.383220 -0.079241   \n",
       "56024   14.0  159.95  0.929003 -0.262584 -0.026089  0.596927 -0.469012   \n",
       "217153  16.0   44.22  1.831384 -0.814800 -0.727692 -0.075094  0.028202   \n",
       "\n",
       "              V6        V7        V8  ...       V19       V20       V21  \\\n",
       "183716 -0.986382  0.698731 -0.284807  ...  0.084549 -0.107484 -0.041904   \n",
       "145898  0.874183  0.332492  0.540016  ...  1.614699  0.352808 -0.159933   \n",
       "247003 -1.130912  0.177956 -0.321792  ...  0.201919 -0.222333 -0.299237   \n",
       "56024  -1.022218  0.463968 -0.176288  ...  0.278433  0.152181 -0.236715   \n",
       "217153  1.380668 -0.941283  0.502667  ... -1.051171 -0.240155  0.242974   \n",
       "\n",
       "             V22       V23       V24       V25       V26       V27       V28  \n",
       "183716  0.058986 -0.103786  0.050964  0.416364  0.694420 -0.082845 -0.028126  \n",
       "145898 -0.261357  0.186308  0.718244 -0.911284 -0.093219 -0.282263 -0.229368  \n",
       "247003 -0.732516  0.322038 -0.112259 -0.281414  0.205225 -0.071557 -0.061429  \n",
       "56024  -1.149710  0.035834  0.504788  0.139781  0.095334 -0.102652  0.025986  \n",
       "217153  0.905053  0.189693 -0.540499 -0.358400  0.202675  0.048368 -0.044683  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prepare the dataset for modeling;\n",
    "# extract the features and labels, perform a quick train/test split\n",
    "label = df['Class']\n",
    "pca_cols = [col for col in df.columns if col.startswith('V')]\n",
    "input_cols = ['hour', 'Amount'] + pca_cols\n",
    "df = df[input_cols]\n",
    "\n",
    "df_train, df_test, y_train, y_test = train_test_split(\n",
    "    df, label, stratify=label, test_size=0.35, random_state=1)\n",
    "\n",
    "print('training data dimension:', df_train.shape)\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "labels distribution: [0.99827251 0.00172749]\n",
      "Fraud is 0.1727485630620034% of our data\n"
     ]
    }
   ],
   "source": [
    "# we'll be using linear models later, hence\n",
    "# we standardize our features to ensure they are\n",
    "# all at the same scale\n",
    "standardize = StandardScaler()\n",
    "X_train = standardize.fit_transform(df_train)\n",
    "X_test = standardize.transform(df_test)\n",
    "\n",
    "label_distribution = np.bincount(label) / label.size\n",
    "print('labels distribution:', label_distribution)\n",
    "print('Fraud is {}% of our data'.format(label_distribution[1] * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Class Weighting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With scikit-learn, we can give higher weights to the minority class (the model will be penalized more when misclassifying a minority class) by modifying the `class_weight` argument during model initialization. Let's see what affect will this have with our model. The following code chunk manually selects a range of weights to boost the minority class and tracks various metrics to see the model's performance across different class weighting values.\n",
    "\n",
    "Note that the following section assumes knowledge of model performance metric such as precision, recall and AUC. The following link contains resources into those concepts if needed. [Notebook: AUC (Area under the ROC curve and precision/recall curve) from scratch](http://nbviewer.jupyter.org/github/ethen8181/machine-learning/blob/master/model_selection/auc/auc.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eRQsGAAAwAwYp+6rq8CR3TrJ/kn/p7ouHyAEAAMCSeFdG67R/I8ltttm2u1YtVigAAIBZMJGyr6ruluTZSb7c3S/fZt8vJvmzJAeON11VVb/a3adOIgsAAACD+2JGxd7Z29kGAADAXpjUlX2/mOSxSf5l/saqOi7Jm8bn3ZRkc5IDkry5qr7S3WdOKA8AAAAD6e47L2QbAAAAu29uQsf9mfHz+7fZ/rSMir5/SnJYkkOSnDbe9pwJZQEAAAAAAICZNKmy70YZXbX3vW22PzCj27T8QXdf3t3XJPmd8b57TigLAABM3JbNm3PIJefkLuvPyDFXbfvPYGBbVfWIqvr53Rh//6p6xCQzAQAATKNJ3cbzBkku6+7r1l+oqhskOSHJpZl3e8/uPqeqrkxy7ISyAADAxP3tKc/LHTdelSTZkso1ly+4w4CV6l1Jvp/kmAWO//MkN87k5rEAAABTaVJX9l2R5OCq2m/etq1X7n1mfgk4dk1GVwICAMBU2jQu+pJkLp2N6y8cMA1MjZrweJZQ+W8HAAAGMamy7z8ymoQ9ct62X87oFp6fmj+wqg5KcnBGv+gEAICpccC6g4eOACvJwUmuHjoEAADAcjOpsu+0jMq+v6iq11fVe5I8OMm1Sf52m7H3GI/95oSyAADARNzziU/J8Xe9R+5w/wflsX/wslx24JFDR4KZVFX3S3JIku8MnQUAAGC5mdRaB3+W5OFJfjbJr+X6W638YXefs83Yx2V0xd8nJpQFAAAm4sib3SIP+a3nz9viHnawI1X19CRP32bzYVX1lZ19LKOS7+iM5o061h9jAAAgAElEQVTvm1A8AACAqTWRsq+7N1XVyUken+RuSTYk+cfu/uf546pq3yRrMpqwvX8SWQAAAFgWjkxy23nvO8m+22zbkS1J3pvkRYsfCwAAYLpN6sq+dPfmJH8zfuxozKYkvzCpDAAAACwbpyY5Y/y6MvrR5/okT9rJZ7Zk9OPRr3f3RZONBwAAMJ0mVvYBAADAVt39zcxbq72qLk7y/e7+h+FSAQAATD9lHwAAAEuuuw8fOgMAAMAs2Ouyr6o+MX55Tnc/eZttu6O7++S9zQMAAAAAAAArxWJc2Xev8fPXt7Ntd/ReJwEAAGDZqaqTxi+v6u6vbbNtt3T3FxctGAAAwAxYjLLvyePnS7ezDQAAAM7I6AeeX09y4jbbdkfHchTLWA0dAAAAVqS9niR191sWsg0AAIAV6+KMirpLt7ONGVG6PgAAGIRfRAIAADBR3X34QrYx5bR9AAAwiLmhAwAAAAAAAAB7ZiJlX1Xdq6q+VVV/uYCxbx2P/ZlJZAEAAAAAAIBZNakr+34xyU2TvG8BYz+Q5GbjzwAAAECq6olV9ZdV9TdV9YSh8wAAACxXkyr77p7RQusfX8DYD4zHurIPAABghaiqJ1fVNVX1tu3sOy3Jm5M8OckTkvx1Vb1jiSMCAABMhUmVfTdOckl3X7Grgd19eZL1SY6ZUBYAAACWnwcmWZXkR8q+qrp/kkclqST/kOSdSbYkeXRVPXqpQwIAACx3kyr7kmTNbo7dd1JBAAAAWHZuN37+zDbbfymju7+8srsf0t2PTfKbGZV/v7x08ViYHjoAAACseJMq+85JsrqqTtrVwKq6U0Zl33cnlAUAAIDl58gkV3b3+m2232f8/L/nbXvT+HmXc0wAAICVZlJl30cy+tXly6tq1Y4Gjfe9PKOfAn5kQlkAAABYfg7YdkNVHZfk8CTndPe3tm4fLxGxPskNli4eAADAdJhU2fenSa5K8t+TfLSq7rztgKq6a5KPj8dcneRVE8oCAADA8nNhkgOq6obztj1g/Pyv2xm/f5INE08FAAAwZSZS9nX3uUmelGRzknsm+VxVXVBVXxg/LshoXYafHY/55e4+ZxJZAAAAWJZOHz8/P0mq6uAkz8zozi8fnT+wqo7N6ErA85YyIAAAwDSY1JV96e53J7lXkjMyuqXnYUnuOH4cNt72+ST37O7TJpUDAACG8IMvfzaXX/Nv2bj+Nbnmsnenu4eOBMvN6zOaFz6zqs5P8p0kt0pyfpJ3bTN26zp+X1q6eAAAANNhn0kevLs/neSnqurWSe6WZOvtWc5P8tnu/sYkzw8AAEM5+5MfuO71lmvPyZWXHTNgGlh+uvujVfVbSV6S5Ijx5u8meXx3X7XN8CePnz+2VPkAAACmxUTLvq3GpZ5iDwCAmdZVO9y3ZcuWJUwC06G7/7Sq/k9Gd4DZkOTM7r56/piq2jfJG5L8RZIP/PhRAAAAVrYlKfsAAGAluPjQm2fd5T9Ikhxys+Nz6dnfSefqXXwKVrbuviTJJ3eyf1OSU5cuEQAAwHSZaNlXVeuSPDXJfZPcOMma7r7lvP0HJ3loRguwv7UtZAIAwBT79k3+Wz67+ZhcuWpN3vGsB+Tjz31mNreyDwAAAJiciZV9VXX3JO/OaJ2+rfcz+pEyr7svrapfT3L7JBck+dCk8gAAwMRV5YL9j9j1OOA6VTWX5BFJHpPkpFy/ft8FSb6Y5G+T/F13uxcuAADAdsxN4qBVdWxGayncKMmHkzwpyfodDP/zjMrAh04iCwAAAMtTVd04yWcyKvQemeQWSdaOH7dI8qgkpyX5dFXdZKicLExlx+uWAgAAkzOpK/t+O8mhSd7W3U9Mkqr6kx2M/cfx890mlAUAAIBlpqoOTPLxJMcl2ZLkH5J8Ism54yHHJvnvSR6Y5K5JPlpVd+zuKweICwAAsGxNquz7uYxu2fnCXQ3s7u9W1VVJbj6hLAAAACw/v55R0Xdekod19xnbGfPqqrpLkveOx/56kpcsXUQAAIDlbyK38Uxy4yRXdPfZCxx/ZZI1E8oCAADA8vPIjH4k+pQdFH1Jku4+PclTM1r+4VFLlI094S6eAAAwiEmVfVcn2b+qdvlP/apaneSQJJdMKAsAAADLz3FJruruD+9qYHf/Y0Y/Ej1+4qkAAACmzKTKvv/M6BahJy5g7IOTrEry1QllAQAAYPmZy2itvoXquHYMAADgx0yq7HtvRpOw39vZoKo6KsmfZDRpe+eEsgAAALD8fDvJgVX107saWFU/k+TA8WcAAACYZ1Jl36uTfCfJY6rqb6rqjhn/ArOq1lbVbavqt5N8KclNknwtyZsmlAUAAIDl530ZzRPfVFU339Ggqjo+o/liJ/n7JcoGAAAwNfaZxEG7+4qq+rkkH0zyhCSPn7d7/tp8leRbSR7S3ZsmkQUAAIBl6X8l+eWM1u47s6renuRTSb6XZHVGPwy9d5JHZDR3PW/8GQAAAOaZSNmXJN39taq6fZL/meRJSY7dZsj5Sd6c5GXdfemkcgAAALD8dPf6qrpvRstAHJfkyePHtirJN5M8rLsv2c5+AACAFW1iZV+SdPeGJC9I8oKqOjbJURndOvT87j57kucGAABgeevu/6iqn8yo5HtkkpOSHDLefUmSLyZ5V5I3d/fGYVICAAAsbxMp+6rqIeOXn+7uC5Oku89Ncu4kzgcAAMvd3HkX5/svfGGuPP2MrLn97XPUS1+SmpvUEtowPcYl3v8eP1JVq8bbNw+ZCwAAYFpM6sq+9ya5NskNJnR8AACYKnOf/3ouueDbSZJrzj47hz7piVlz4okDp4LlR8kHAACweyZV9l2cJN19+YSODwAAU23LFVcMHQEGUVWHJnl8kjslWZfR7To/l+Tt5pAAAAC7b1Jl31lJ7lFV68br9gEAwIqzZS7J1muUDlqdVZsOyeZLLhkyEgyqqn4uyakZlXzzPTnJH1XVI7r735Y+GQAAwPSa1CIhf5FkVZJnTej4AACw7G1eVde9vvYJJ2f/444bMA0Mq6pukeSdSQ5OUkk2JPlakivG749I8ndVdfhgIQEAAKbQRMq+7n5bktcmOaWqXlxV1u4DAGDFWz93eL5z7MlZf7DSjxXpOUkOSPKDJD/f3Yd29227e11Gt/XckOSwJL8yYEYAAICpM5HbeFbVJ8Yvr0zy/CS/U1X/leSCXH8jo211d588iTwAADC0b//r2nz7gAcnxyXpLTn+yi05cOhQsLROTtJJ/r/u/tD8Hd39jvFafq8fj3vpAPkAAACm0qTW7LvXds5zwvixIz2hLAAAMLgt185lbuu/vmsuGy7tHDFoIlhyN0myJckHd7D/fRmVfTddskQAAAAzYFJl35MndFwAAJgaXTu6qQWsSAcl+UF3X7O9nd39vapKRrf6ZBpV7XoMAACw6CZS9nX3WyZxXAAAmCbX7HdJ1lw9+vL7Zj+zIZf+a2d9b0mtOjTJmmHDwfKlMQIAANgNi1r2VdX+SR6W5E5J1iW5JMnnkry/u69dzHMBAMByt3luU5L9kiQ/+MrnsuGyS5JsTmr/bNr0rEGzAQAAALNh0cq+qrpHkncmudF2dp9dVQ/r7q8u1vkAAGCabPjeRde/6atz6frzhwsDwzmsqr6yF2O6u2+/2KFYHC7JBACAYSxK2VdVxyT5QJKDM/r3/ZYkFyY5Yvz+5kk+WFW37e5LF+OcAACw3G3Zp3e8s3eyD2bXvkluuxdj/B8OAADANhbryr7nJDkkyfrx69O6+5qqWp3kqUlenuToJE9J8qpFOicAACxrP7zZ5Zm7clU27rc5P3XSXXPOP52Ta7dcMnQsGMorhw7ApM0NHQAAAFakxSr77pvRLyyf3d1v27qxuzcmed249HtFkvtF2QcAwAqx4Yir86/3/l6S5LF3OjHf/efvD5wIhtPdvz10BgAAgFm0WD+7u0VGZd+7d7D/nfPGAQAAAAAAAItgscq+tUkuGF/J92O6+5zxywMX6XwAAADAclI1dAIAAFiRFvOG+gtZKN2//AEAAGBG9IK+CgAAACbJ6tkAAADA3vPzXgAAGMQ+i3isG1TVJ/ZiTHf3yYuYBwAAlo2/OPsDudWWfa97f21fO2AaAAAAYFYsZtm3X5J77cUY9/4AAGBm/ctFX82tcsfr3q/fuH7ANAAAAMCsWKyy7y2LdBwAAJgZ++1k35b2WzcAAAB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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 501,
       "width": 893
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 8))\n",
    "ax1 = fig.add_subplot(1, 2, 1)\n",
    "ax1.set_xlim([-0.05, 1.05])\n",
    "ax1.set_ylim([-0.05, 1.05])\n",
    "ax1.set_xlabel('Recall')\n",
    "ax1.set_ylabel('Precision')\n",
    "ax1.set_title('PR Curve')\n",
    "\n",
    "ax2 = fig.add_subplot(1, 2, 2)\n",
    "ax2.set_xlim([-0.05, 1.05])\n",
    "ax2.set_ylim([-0.05, 1.05])\n",
    "ax2.set_xlabel('False Positive Rate')\n",
    "ax2.set_ylabel('True Positive Rate')\n",
    "ax2.set_title('ROC Curve')\n",
    "\n",
    "f1_scores = []\n",
    "recall_scores = []\n",
    "precision_scores = []\n",
    "pos_weights = [1, 10, 25, 50, 100, 10000]\n",
    "for pos_weight in pos_weights:\n",
    "    lr_model = LogisticRegression(class_weight={0: 1, 1: pos_weight})\n",
    "    lr_model.fit(X_train, y_train)\n",
    "\n",
    "    # plot the precision-recall curve and AUC curve\n",
    "    pred_prob = lr_model.predict_proba(X_test)[:, 1]\n",
    "    precision, recall, _ = precision_recall_curve(y_test, pred_prob)\n",
    "    tpr, fpr, _ = roc_curve(y_test, pred_prob)\n",
    "\n",
    "    ax1.plot(recall, precision, label=pos_weight)\n",
    "    ax2.plot(tpr, fpr, label=pos_weight)\n",
    "\n",
    "    # track the precision, recall and f1 score\n",
    "    pred = lr_model.predict(X_test)\n",
    "    f1_test = f1_score(y_test, pred)\n",
    "    recall_test = recall_score(y_test, pred)\n",
    "    precision_test = precision_score(y_test, pred)\n",
    "    f1_scores.append(f1_test)\n",
    "    recall_scores.append(recall_test)\n",
    "    precision_scores.append(precision_test)\n",
    "\n",
    "ax1.legend(loc='lower left')    \n",
    "ax2.legend(loc='lower right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A good classifier would have a PR (Precision/Recall) curve closer to the upper-right corner and a ROC curve to the upper-left corner. Based on the plot above, we can see that while both curves uses the same underlying data, i.e. the real class labels and the predicted probability, the two charts can tell different stories, with some weights seem to perform better based on the precision/recall curve's chart.\n",
    "\n",
    "To be explicit, different settings of the `class_weight` argument all seem to perform pretty well for ROC curve, but some poorly for PR curve. This is due to the fact that for ROC curve, one of the axis shows the false positive rate (number of false positives / total number of negatives), and this ratio won't change much when the total number of negatives is extremely large. Whereas for PR curve, one of the axis, precision (number of true positives / total number of predicted positives), is less affected by this.\n",
    "\n",
    "Another way to visualize the model's performance metric is to use a bar-plot to visualize the precision/recall/f1 score at different class weighting values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 418,
       "width": 563
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def score_barplot(precision_scores, recall_scores, f1_scores, pos_weights, figsize=(8, 6)):\n",
    "    \"\"\"Visualize precision/recall/f1 score at different class weighting values.\"\"\"\n",
    "    width = 0.3\n",
    "    ind = np.arange(len(precision_scores))      \n",
    "    fig = plt.figure(figsize=figsize)\n",
    "    ax = fig.add_subplot(111)\n",
    "    b1 = ax.bar(ind, precision_scores, width, color='lightskyblue')\n",
    "    b2 = ax.bar(ind + width, recall_scores, width, color='lightcoral')\n",
    "    b3 = ax.bar(ind + (2 * width), f1_scores, width, color='gold')\n",
    "\n",
    "    ax.set_xticks(ind + width)\n",
    "    ax.set_xticklabels(pos_weights)\n",
    "    ax.set_ylabel('score')\n",
    "    ax.set_xlabel('positive weights')\n",
    "    ax.set_ylim(0, 1.3)\n",
    "    ax.legend(handles=[b1, b2, b3], labels=['precision', 'recall', 'f1'])\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "score_barplot(precision_scores, recall_scores, f1_scores, pos_weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Judging from the plot above, the can see that when the weight's value is set at 10, we seem to have strike a good balance between precision and recall (this setting has the highest f1 score, we'll have a deeper discussion on f1 score in the next section), where our model can detect 80% of the fraudulent transaction, while not annoying a bunch of customers with false positives. Another observation is that if we were to set the class weighting value to 10,000 we would be able to increase our recall score at the expense of more mis-classified legit cases (as depicted by the low precision score)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 418,
       "width": 563
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this code chunk shows the same idea applies when using tree-based models\n",
    "f1_scores = []\n",
    "recall_scores = []\n",
    "precision_scores = []\n",
    "pos_weights = [1, 10, 100, 10000]\n",
    "for pos_weight in pos_weights:\n",
    "    rf_model = RandomForestClassifier(n_estimators=50, max_depth=6, n_jobs=-1,\n",
    "                                      class_weight={0: 1, 1: pos_weight})\n",
    "    rf_model.fit(df_train, y_train)\n",
    "\n",
    "    # track the precision, recall and f1 score\n",
    "    pred = rf_model.predict(df_test)\n",
    "    f1_test = f1_score(y_test, pred)\n",
    "    recall_test = recall_score(y_test, pred)\n",
    "    precision_test = precision_score(y_test, pred)\n",
    "    f1_scores.append(f1_test)\n",
    "    recall_scores.append(recall_test)\n",
    "    precision_scores.append(precision_test)\n",
    "\n",
    "score_barplot(precision_scores, recall_scores, f1_scores, pos_weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## F1 Score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The formula for F1 score is:\n",
    "\n",
    "\\begin{align}\n",
    "F1 &= 2 * \\frac{\\text{precision} * \\text{recall}}{\\text{precision} + \\text{recall}}\n",
    "\\end{align}\n",
    "\n",
    "F1 score can be interpreted as a weighted average or harmonic mean of precision and recall, where the relative contribution of precision and recall to the F1 score are equal. F1 score reaches its best value at 1 and worst score at 0.\n",
    "\n",
    "When we create a classifier, often times we need to make a compromise between the recall and precision, it is kind of hard to compare a model with high recall and low precision versus a model with high precision but low recall. F1 score merge these two metrics into a single measure that we can use to compare two models. This is not to say that a model with higher F1 score is always better as it depends on the use case, more on this in the conclusion section.\n",
    "\n",
    "To understand the rationale behind harmonic means, let's digress from machine learning evaluation metrics and take a look at a canonical example of using harmonic means. Consider a trip to the grocery store & back:\n",
    "\n",
    "- On the way there we drove 30 mph the entire way.\n",
    "- On the way back traffic was crawling, so we instead drove 10 mph the entire way.\n",
    "- We took the same route and covered the same amount of ground (5 miles) each way.\n",
    "\n",
    "What was our average speed across this entire trip's duration? When prompted to calculate the average of something, we might naively turn to arithmetic mean to 30 mph and 10 mph and proudly declare that the average speed for the whole trip was 20 mph. But if we step back and think about it for a moment, we'll realize that because we traveled faster on our way there, we covered the 5 miles quicker & spent less time overall traveling at that speed, thus our average speed across our entire trip should be closer to 10 mph because we spent longer traveling at that speed. In order to calculate the arithmetic mean correctly here, we have to first calculate the amount of time spent traveling at each rate, then weight our mean calculation accordingly.\n",
    "\n",
    "- Trip There: (at 30 mph)\n",
    "    - 30 miles per 60 minutes = 1 mile every 2 minutes = 1/2 mile every minute\n",
    "    - 5 miles at 1/2 mile per minute = 5 / 1/2 = 10 minutes\n",
    "    - Trip There time = 10 minutes\n",
    "- Trip Back: (at 10 mph)\n",
    "    - 10 miles per 60 minutes = 1 mile every 6 minutes = 1/6 miles every minute\n",
    "    - 5 miles at 1/6 mile per minute = 5 / 1/6 = 30 minutes\n",
    "    - Trip Back time = 30 minutes\n",
    "- Total trip time = 10 + 30 = 40 minutes\n",
    "    - Trip there / total trip = 10 / 40 minutes = .25\n",
    "    - Trip back / total trip = 30 / 40 minutes = .75\n",
    "- Weighted Arithmetic Mean = (30 mph x .25) + (10 mph x .75) = 7.5 + 7.5 = 15 \n",
    "\n",
    "We now get an average speed of 15 mph, which matches the intuition that the number should be lower than our unweighted arithmetic mean of 20 mph. Now let's look at the same problem from a harmonic mean perspective. The harmonic mean can be described in words as: the reciprocal of the arithmetic mean of the reciprocals of the dataset. That's a lot of reciprocal flips there ..., the notation for the computation is:\n",
    "\n",
    "\\begin{align}\n",
    "\\text{Harmonic mean} &= \\big( \\frac{\\sum_{i=1}^n x_i^{-1}}{n} \\big)^{-1}\n",
    "\\end{align}\n",
    "\n",
    "Now back our original example, the harmonic mean of 30 mph and 10 mph:\n",
    "\n",
    "- Arithmetic mean of reciprocals = 1/30 + 1/10 = 4/30 / 2 = 4/60 = 1/15\n",
    "- Reciprocal of arithmetic mean = 1 / 1/15 = 15/1 = 15\n",
    "\n",
    "Our true average rate of travel, automagically adjusted for time spent traveling in each direction = 15 mph! In this example, using harmonic mean helps us find relationship with datasets of rates/ratios/fractions over different length/periods. By using reciprocals, it already accounts for the proportion of time that was implicit in the rates.\n",
    "\n",
    "With this knowledge in mind, let's turn our heads back to F1 score's calculation. Precision and recall both have true positives in the numerator, but they have different denominators. In order for an average to be valid, we need the values to be in the same scaled units. In our traveling distance example miles per hour need to be compared over the same number of hours. Thus, to take the average of precision and recall really only makes sense to average their reciprocals (adjusts for scale), thus the harmonic mean.\n",
    "\n",
    "If the business case warrants it, we may want to put more weight on recall than precision or vice-versa. In that case a more general version of the F score called F beta score could be useful. \n",
    "\n",
    "\\begin{align}\n",
    "F_{\\beta}&= (1+\\beta^2) * \\frac{\\text{precision} * \\text{recall}}{\\beta^2*\\text{precision} + \\text{recall}}\n",
    "\\end{align}\n",
    "\n",
    "With $\\beta>1$ you focus more on recall, with $0<\\beta<1$ you put more weight on precision.\n",
    "For example, commonly used F2 score puts 2x more weight on recall than precision. You can find more information on the subject here [Blog: 24 Evaluation Metrics for Binary Classification (And When to Use Them)](https://neptune.ml/blog/evaluation-metrics-binary-classification) "
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    "## Conclusion"
   ]
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    "To sum it up, when using model-based metrics to evaluate a imbalanced classification problem, it is often times recommended to look at the precision and recall score to fully evaluate the overall effectiveness of a model.\n",
    "\n",
    "A model with high recall but low precision score returns many positive results, but most of its predicted labels are incorrect when compared to the ground truth. On the other hand, a model with high precision but low recall score returns very few results, but most of its predicted labels are correct when compared to the ground-truth. An ideal scenario would be a model with high precision and high recall, meaning it will return many results, with all results labeled correctly. Unfortunately, in most cases, precision and recall are often in tension. That is, improving precision typically reduces recall and vice versa.\n",
    "\n",
    "So how do we know if we should sacrifice our precision for more recall, i.e. catching fraud? That is business decisions come into play. If the cost of missing a fraud highly outweighs the cost of canceling a bunch of legit customer transactions, i.e. false positives, then perhaps we can choose a weight that gives us a higher recall rate. Or maybe catching 80% of fraud is good enough for the business, in that case, we can also minimize the \"user friction\" by keeping our precision high."
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    "# Reference\n",
    "\n",
    "- [Notes: The Harmonic Mean](http://jwilson.coe.uga.edu/EMT725/HM/HM.html)\n",
    "- [Blog: \n",
    "Classification: Precision and Recall](https://developers.google.com/machine-learning/crash-course/classification/precision-and-recall)\n",
    "- [Blog: 24 Evaluation Metrics for Binary Classification (And When to Use Them)](https://neptune.ml/blog/evaluation-metrics-binary-classification)\n",
    "- [Blog: On Average, You’re Using the Wrong Average: Geometric & Harmonic Means in Data Analysis](https://towardsdatascience.com/on-average-youre-using-the-wrong-average-geometric-harmonic-means-in-data-analysis-2a703e21ea0)\n",
    "- [Kaggle Kernel: Imbalanced data & why you should NOT use ROC curve](https://www.kaggle.com/lct14558/imbalanced-data-why-you-should-not-use-roc-curve)\n",
    "- [Stackoverflow: what is f1-score and what its value indicates?](https://stackoverflow.com/questions/45963174/what-is-f1-score-and-what-its-value-indicates)\n",
    "- [Stackoverflow: Why is the F-Measure a harmonic mean and not an arithmetic mean of the Precision and Recall measures?](https://stackoverflow.com/questions/26355942/why-is-the-f-measure-a-harmonic-mean-and-not-an-arithmetic-mean-of-the-precision)"
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