examples/0.OOP_in_scikit-learn_ML.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### [HOME](../README.md) | [NEXT](1.raw_course_specification.ipynb)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Object Oriented Paradigm in scikit-learn Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Jupyter Notebook will explore the `scikit-learn` library. However, the objective is not so much to learn about `scikit-learn`, but to explore how a professionally developed, widely used library uses the organizational principles of object oriented programming to provide a enhanced user experience.\n",
    "\n",
    "The main theme of our explorations will be the power of **Providing a Consistent Interface**.  One of the fundamental benefits of OOP is the ability to program a simple interface that is a facade to a complex implementation. Sklearn is a very good example of this important programming philosophy.\n",
    "\n",
    "Students should be able to identify OOP principals such as generalisation, inheritance, abstraction, polymorphism and encapsulation.\n",
    "\n",
    "Students can look at the [scikit-learn source](https://github.com/scikit-learn/scikit-learn) to see these principals at the code level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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 LinearRegression, LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regressors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In a Supervised Learning problem we have some data $X$ (often called the **features**) which we want to use to make a prediction of another measurement $y$ (often called the **target**).  For example:\n",
    "\n",
    "  - Use the history of all board game ratings on [Board Game Geek](https://boardgamegeek.com/) ($X$) to predict the rating a user will give a new game ($y$).\n",
    "  - Use the history of all customers of a car insurance company ($X$) to predict the likelihood of a new customer being involved in a car accident ($y$).\n",
    "  - Predict which type of animal ($y$) appears in a black and white photo taken by a wildlife camera ($X$).\n",
    "  - Predict whether a flight will be delayed and, if so, how delayed it will be.\n",
    "  \n",
    "The general theme is that:\n",
    "\n",
    "> We **fit** a model to some data ($X, y$), and then use the model to **predict** given some new data.\n",
    "\n",
    "Let's look at two examples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression: Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our first example is **linear regression**.  In linear regression we have feature data $X$, and our target response is a **continuous** measurement."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The data set we will use to demonstrate linear regression contains measurements on a single species of insect captured on two continents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
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   "outputs": [
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<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>wingsize</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>38.6</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>40.7</td>\n",
       "      <td>907</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>40.9</td>\n",
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       "      <td>0</td>\n",
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      "text/plain": [
       "   continent  latitude  wingsize  sex\n",
       "0          1      35.5       901    0\n",
       "1          1      37.0       896    0\n",
       "2          1      38.6       906    0\n",
       "3          1      40.7       907    0\n",
       "4          1      40.9       898    0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insects = pd.read_csv('./data/insects.csv', sep='\\t')\n",
    "insects.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suppose we want to predict an insects wing size given the other measurements we have.  Since wing size is a continuous measurement, linear regression is an appropriate way to approach this problem.\n",
    "\n",
    "All **regression objects** are used in the same way in sklearn.\n",
    "\n",
    "1\\. Create a regression object of the appropriate type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "insects_regression = LinearRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To explain this further, `LinearRegression` is a **class**, and calling it `__init__`'s an **object** which is an **instance of the LinearRegression class**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2\\. Use the `fit` method on the regression object to train the model using your data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
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   "outputs": [
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       "<style>#sk-container-id-1 {\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-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 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-1 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-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 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-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 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-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 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-1 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-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 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-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 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-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</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.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_insects = insects[['continent', 'latitude', 'sex']]\n",
    "y_insects = insects['wingsize']\n",
    "insects_regression.fit(X_insects, y_insects)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above we trained a linear model to create an equation of the form:\n",
    "\n",
    "$$ \\text{WingSize} \\approx a + b \\times \\text{Continent} + c \\times \\text{Latitude} + d \\times \\text{Sex} $$\n",
    "\n",
    "When we used `fit`, the model used the data we supplied to compute the correct values of the numbers $a, b, c, d$.  We can now pull these numbers out of the model object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a = 836.16\n"
     ]
    }
   ],
   "source": [
    "print(\"a = {:2.2f}\".format(insects_regression.intercept_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b, c, d = [ -4.12890383   1.79260932 -98.85714286]\n"
     ]
    }
   ],
   "source": [
    "print(\"b, c, d = {}\".format(insects_regression.coef_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3\\. Use the `predict` method to compute predicted values your data (could be the data you used to train the model, or another dataset entirely."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wing_size_predictions = insects_regression.predict(X_insects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[895.67349147 898.36240545 901.23058036 904.99505993 905.35358179\n",
      " 908.04249577 912.70328    915.92997677 919.51519541 921.30780472]\n"
     ]
    }
   ],
   "source": [
    "print(wing_size_predictions[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calling the `predict` method uses the equation to make predictions about wing size.  This is very useful if we had data on insects that we **didn't** know the wingsize of. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For fun, let's plot the true data and the predicted data in two scatterplots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Predicted Wing Span')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "axs[0].scatter(insects.latitude, insects.wingsize, s=40)\n",
    "axs[0].set_title(\"Actual Data\")\n",
    "axs[0].set_xlabel(\"Latitude\")\n",
    "axs[0].set_ylabel(\"Actual Wing Span\")\n",
    "\n",
    "\n",
    "axs[1].scatter(insects.latitude, wing_size_predictions, s=40)\n",
    "axs[1].set_title(\"Predicted Data\")\n",
    "axs[1].set_xlabel(\"Latitude\")\n",
    "axs[1].set_ylabel(\"Predicted Wing Span\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Summary:** A `LinearRegression` uses the methods `fit` and `predict` to adapt itself to data, and to then make predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification: Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our second example is **logistic regression**.  In logistic regression we have feature data $X$, and our target $y$ is a **binary** measurement.\n",
    "\n",
    "A **binary** measurement can only take two values, which are traditionally labeled as $0$ and $1$.\n",
    "\n",
    "Our example will use a dataset of wells in Bangladesh.  Many family wells are contaminated with arsenic; a team of scientists visited and measured:\n",
    "\n",
    "  - The arsenic levels in each family's well.\n",
    "  - The distance to the nearest safe well.\n",
    "  - The level of education of the household.\n",
    "  - The household's involvement in the community.\n",
    "  \n",
    "After discussing the level of arsenic with each family, the families with unsafe levels of arsenic were advised to switch to a nearby safe well.  Years later, the team followed up, and measured which families switched wells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wells = pd.read_csv('./data/wells.dat', sep=' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "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>id</th>\n",
       "      <th>switch</th>\n",
       "      <th>arsenic</th>\n",
       "      <th>dist</th>\n",
       "      <th>assoc</th>\n",
       "      <th>educ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.36</td>\n",
       "      <td>16.826000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.71</td>\n",
       "      <td>47.321999</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.07</td>\n",
       "      <td>20.966999</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1.15</td>\n",
       "      <td>21.486000</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.10</td>\n",
       "      <td>40.874001</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  switch  arsenic       dist  assoc  educ\n",
       "0   1       1     2.36  16.826000      0     0\n",
       "1   2       1     0.71  47.321999      0     0\n",
       "2   3       0     2.07  20.966999      0    10\n",
       "3   4       1     1.15  21.486000      0    12\n",
       "4   5       1     1.10  40.874001      1    14"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wells.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use a logistic regression to predict whether a family will switch based on the other data the team measured.\n",
    "\n",
    "The first step is to `__init__` an instance (or object) of the `LogisticRegression` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wells_regression = LogisticRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the `fit` method of the `LogisticRegression` class to train the model using our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\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-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 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-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 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-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 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-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 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-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2\" 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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" 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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We don't need the i'd column, so drop it.\n",
    "X_wells_names = np.array(['arsenic', 'dist', 'assoc', 'educ'])\n",
    "X_wells = wells[X_wells_names]\n",
    "# The response is already encoded as 0's, and 1's.\n",
    "y_wells = wells['switch']\n",
    "wells_regression.fit(X_wells, y_wells)"
   ]
  },
  {
   "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": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 1 1 1 1 1 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "wells_predictions = wells_regression.predict(X_wells)\n",
    "print(wells_predictions[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally (and in many cases more usefully), the `LogisticRegression` has a `predict_proba` method that produces a predicted **probability** that a family will switch well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.311407   0.688593  ]\n",
      " [0.5618803  0.4381197 ]\n",
      " [0.26006313 0.73993687]\n",
      " [0.33248748 0.66751252]\n",
      " [0.38667832 0.61332168]\n",
      " [0.21450595 0.78549405]\n",
      " [0.36540595 0.63459405]\n",
      " [0.21669702 0.78330298]\n",
      " [0.31473513 0.68526487]\n",
      " [0.44449671 0.55550329]]\n"
     ]
    }
   ],
   "source": [
    "wells_probabilities = wells_regression.predict_proba(X_wells)\n",
    "print(wells_probabilities[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `predict_proba` method creates a two column array, the first is the probability the family will not switch, the second is the probability they will.  This means that the rows of this matrix always add to $1$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., ..., 1., 1., 1.], shape=(3020,))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(wells_probabilities, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the `predict_proba` method to plot the actual data, along with the predicted probabilities that a family will switch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Probability of Switching')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "axs[0].scatter(wells.dist, wells.switch, s=40, alpha=0.33)\n",
    "axs[0].set_title(\"Actual Data\")\n",
    "axs[0].set_xlabel(\"Distance to Safe Well\")\n",
    "axs[0].set_ylabel(\"Did Resident Switch?\")\n",
    "\n",
    "\n",
    "axs[1].scatter(wells.dist, wells_probabilities[:, 1], s=40, alpha=0.33)\n",
    "axs[1].set_title(\"Predicted Data\")\n",
    "axs[1].set_xlabel(\"Distance to Safe Well\")\n",
    "axs[1].set_ylabel(\"Probability of Switching\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bring them back together"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two regression classes `LinearRegression` and `LogisticRegression` both inherit supper classes from the same `_base` which has generalised many of the core functionality of the two subclasses and thus have many overriding polymorphic methods in common:\n",
    "\n",
    "  - Both have a `fit` methods.  The `fit` methods for both take the same arguments `X` and `y`.\n",
    "  - Both have a `predict` method.  The `predict` methods for both take the same argument, `X`.\n",
    "  \n",
    "We can summarize this by saying that **`LinearRegression` and `LogisticRegression` have the same interface**.  They have the same methods that are used to the same conceptual tasks.  This allows us to write code that performs high level tasks using the `fit` and `predict` methods, which can then be used with any object that implements both `fit` and `predict` methods."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}