examples/4.OOP_Logistic_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### [PREV](3.OOP_Polynomial_Liner_Regression.ipynb) | [HOME](../README.md) | [NEXT](5.OOP_KNN.ipynb)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OOP Logistic Regression Implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Jupyter Notebook provides a basic implementation of Logistic Regression for categorisation. The data set contains measurements on a single species of insect captured on two continents. We want to predict an insects continent given the other measurements we have."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1 - Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error,mean_squared_error "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 2 - Import And Visualise The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "continent",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "latitude",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "sex",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "wingsize",
         "rawType": "int64",
         "type": "integer"
        }
       ],
       "conversionMethod": "pd.DataFrame",
       "ref": "745a8fa1-cd7b-4c71-bf1c-3403f450ad28",
       "rows": [
        [
         "0",
         "1",
         "35.5",
         "0",
         "901"
        ],
        [
         "1",
         "1",
         "37.0",
         "0",
         "896"
        ],
        [
         "2",
         "1",
         "38.6",
         "0",
         "906"
        ],
        [
         "3",
         "1",
         "40.7",
         "0",
         "907"
        ],
        [
         "4",
         "1",
         "40.9",
         "0",
         "898"
        ]
       ],
       "shape": {
        "columns": 4,
        "rows": 5
       }
      },
      "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>continent</th>\n",
       "      <th>latitude</th>\n",
       "      <th>sex</th>\n",
       "      <th>wingsize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>35.5</td>\n",
       "      <td>0</td>\n",
       "      <td>901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0</td>\n",
       "      <td>896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>38.6</td>\n",
       "      <td>0</td>\n",
       "      <td>906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>40.7</td>\n",
       "      <td>0</td>\n",
       "      <td>907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>40.9</td>\n",
       "      <td>0</td>\n",
       "      <td>898</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   continent  latitude  sex  wingsize\n",
       "0          1      35.5    0       901\n",
       "1          1      37.0    0       896\n",
       "2          1      38.6    0       906\n",
       "3          1      40.7    0       907\n",
       "4          1      40.9    0       898"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import data as a Pandas DataFrame\n",
    "insects = pd.read_csv('./data/insects.csv', sep='\\t')\n",
    "insects = pd.DataFrame({\n",
    "    'continent': insects['continent'],\n",
    "    'latitude': insects['latitude'],\n",
    "    'sex': insects['sex'],\n",
    "    'wingsize': insects['wingsize']\n",
    "})\n",
    "\n",
    "# Features variable\n",
    "X_insects = insects[['wingsize', 'latitude', 'sex']]\n",
    "# Target variable\n",
    "y_insects = insects['continent']\n",
    "\n",
    "insects.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the data, colour coded by sex\n",
    "ax = plt.scatter(insects.continent, insects.wingsize, c=insects['sex'])\n",
    "plt.xlabel(\"Wingsize\")\n",
    "plt.ylabel(\"Sex\")\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 3: Instantiate a LogisticRegression Object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "insects_regression = LogisticRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 4: Fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insects_regression.fit(X_insects, y_insects)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the regression has been fit, we can use the `predict` method to forecast whether our model thinks a family will switch wells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New insect is:\n",
      "   wingsize  latitude  sex  continent\n",
      "0         1        40    0          1\n"
     ]
    }
   ],
   "source": [
    "# Predict the target for the whole dataset\n",
    "insects_predictions = insects_regression.predict(X_insects)\n",
    "\n",
    "#Predict the target for a new data point\n",
    "new_insect = pd.DataFrame({\n",
    "    'wingsize': [1],\n",
    "    'latitude': [40],\n",
    "    'sex': [0]\n",
    "})\n",
    "new_insect['continent'] = insects_regression.predict(new_insect)\n",
    "print(f\"New insect is:\\n{new_insect}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally (and in many cases more usefully), the `LogisticRegression` has a `predict_proba` method that produces a predicted **probability**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probabilities for new insect are:\n",
      "[[0.00675163 0.99324837]]\n"
     ]
    }
   ],
   "source": [
    "#Predict the probabilities for the whole dataset\n",
    "insects_probabilities = insects_regression.predict_proba(X_insects)\n",
    "\n",
    "#Predict the probabilities for a new data point\n",
    "new_insect = pd.DataFrame({\n",
    "    'wingsize': [1],\n",
    "    'latitude': [40],\n",
    "    'sex': [0]\n",
    "})\n",
    "new_insect_probabilities = insects_regression.predict_proba(new_insect)\n",
    "print(f\"Probabilities for new insect are:\\n{new_insect_probabilities}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 5: Plot the Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Continent Prediction')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the predictions compared to the actual data\n",
    "fig, axs = plt.subplots(1, 3, figsize=(14, 5))\n",
    "\n",
    "axs[0].scatter(insects.wingsize, insects.continent, s=40, c=insects['sex'])\n",
    "axs[0].set_title(\"Actual Data\")\n",
    "axs[0].set_xlabel(\"Wingsize\")\n",
    "axs[0].set_ylabel(\"Continent\")\n",
    "\n",
    "axs[1].scatter(insects.wingsize, insects_probabilities[:, 1], s=40, c=insects['sex'])\n",
    "axs[1].set_title(\"Prediction Probabilities\")\n",
    "axs[1].set_xlabel(\"Wingsize\")\n",
    "axs[1].set_ylabel(\"Continent Probability\")\n",
    "\n",
    "axs[2].scatter(insects.wingsize, insects_predictions, s=40, c=insects['sex'])\n",
    "axs[2].set_title(\"Predicted Data\")\n",
    "axs[2].set_xlabel(\"Wingsize\")\n",
    "axs[2].set_ylabel(\"Continent Prediction\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 6: Evaluate The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 0.4668839798239841\n",
      "MSE: 0.2327758162295096\n"
     ]
    }
   ],
   "source": [
    "mae = mean_absolute_error(y_true=y_insects,y_pred=insects_probabilities[:, 1]) \n",
    "mse = mean_squared_error(y_true=y_insects,y_pred=insects_probabilities[:, 1])\n",
    "print(\"MAE:\",mae) \n",
    "print(\"MSE:\",mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cost of this model is  0.21429\n",
      "The model predicted incorrectly 18 times out of 42 predictions\n",
      "    Target  Predicted result  Loss\n",
      "0        1               1.0     0\n",
      "1        1               1.0     0\n",
      "2        1               1.0     0\n",
      "3        1               1.0     0\n",
      "4        1               1.0     0\n",
      "5        1               1.0     0\n",
      "6        1               1.0     0\n",
      "7        1               0.0     1\n",
      "8        1               0.0     1\n",
      "9        1               0.0     1\n",
      "10       1               0.0     1\n",
      "11       0               1.0     1\n",
      "12       0               1.0     1\n",
      "13       0               1.0     1\n",
      "14       0               1.0     1\n",
      "15       0               1.0     1\n",
      "16       0               0.0     0\n",
      "17       0               0.0     0\n",
      "18       0               0.0     0\n",
      "19       0               0.0     0\n",
      "20       0               0.0     0\n",
      "21       1               1.0     0\n",
      "22       1               1.0     0\n",
      "23       1               1.0     0\n",
      "24       1               1.0     0\n",
      "25       1               1.0     0\n",
      "26       1               1.0     0\n",
      "27       1               1.0     0\n",
      "28       1               0.0     1\n",
      "29       1               0.0     1\n",
      "30       1               0.0     1\n",
      "31       1               0.0     1\n",
      "32       0               1.0     1\n",
      "33       0               1.0     1\n",
      "34       0               1.0     1\n",
      "35       0               1.0     1\n",
      "36       0               1.0     1\n",
      "37       0               0.0     0\n",
      "38       0               0.0     0\n",
      "39       0               0.0     0\n",
      "40       0               0.0     0\n",
      "41       0               0.0     0\n"
     ]
    }
   ],
   "source": [
    "#Optional: Manually Evaluate the loss and cost of the model\n",
    "model_loss = pd.DataFrame({\n",
    "    'Target': y_insects,\n",
    "    'Predicted result': insects_probabilities[:, 1],\n",
    "    'Loss': abs(insects_regression.predict(X_insects).round(2) - y_insects)**2\n",
    "})\n",
    "model_loss[\"Predicted result\"] = model_loss[\"Predicted result\"].round(0)\n",
    "model_loss[\"Loss\"] = model_loss[\"Loss\"].abs()\n",
    "model_cost = 1 / (2 * model_loss.shape[0]) * model_loss['Loss'].sum()\n",
    "\n",
    "print(f\"The cost of this model is  {model_cost:.5f}\")\n",
    "print(f\"The model predicted incorrectly {model_loss['Loss'].sum()} times out of {model_loss.shape[0]} predictions\")\n",
    "print(model_loss)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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