lectures/03_ml-fundamentals.ipynb

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    "# Lecture 3: Machine Learning Fundamentals \n",
    "\n",
    "UBC 2021-22\n",
    "\n",
    "Instructor: Giulia Toti"
   ]
  },
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   "source": [
    "## Imports"
   ]
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   "source": [
    "# import the libraries\n",
    "import os\n",
    "import sys\n",
    "\n",
    "import graphviz\n",
    "import IPython\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import HTML\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "sys.path.append(\"code/.\")\n",
    "from plotting_functions import *\n",
    "\n",
    "# Classifiers\n",
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
    "from utils import *\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "pd.set_option(\"display.max_colwidth\", 200)"
   ]
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   "source": [
    "<br><br>"
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   "source": [
    "## Learning outcomes\n",
    "\n",
    "From this lecture, you will be able to \n",
    "\n",
    "- explain how decision boundaries change with the `max_depth` hyperparameter;\n",
    "- explain the concept of generalization;\n",
    "- appropriately split a dataset into train and test sets using `train_test_split` function;\n",
    "- explain the difference between train, validation, test, and \"deployment\" data;\n",
    "- identify the difference between training error, validation error, and test error;\n",
    "- explain cross-validation and use `cross_val_score` and `cross_validate` to calculate cross-validation error;\n",
    "- recognize overfitting and/or underfitting by looking at train and test scores;\n",
    "- explain why it is generally not possible to get a perfect test score (zero test error) on a supervised learning problem;\n",
    "- describe the fundamental tradeoff between training score and the train-test gap;\n",
    "- state the golden rule;\n",
    "- start to build a standard recipe for supervised learning: train/test split, hyperparameter tuning with cross-validation, test on test set."
   ]
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   "source": [
    "## Announcements\n",
    "\n",
    "- hw2 released. (Due next week Monday at 11:59pm.)\n",
    "    - You are allowed to submit in pairs. \n",
    "    - If you need to find a partner to work with, check out https://piazza.com/class/ky0j51i4ud64t5?cid=5\n",
    "- Advice on keeping up with the material \n",
    "    - Practice! You will find some practice questions [here](https://ml-learn.mds.ubc.ca/). \n",
    "    - Start early on homework assignments.      \n",
    "- Last day to withdraw without a W standing: January 21st, 2022\n",
    "\n",
    "Homework check in: https://www.menti.com/ovui2zsefn"
   ]
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    "<br><br>"
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   "source": [
    "## Recap\n",
    "\n",
    "Last week, we introduced the following concepts:\n",
    "- General idea of what Machine Learning is and its applications\n",
    "- Basic terminology (such as features vs. target, unsupervised vs. supervised learning, classification vs. regression)\n",
    "- Why is it a good idea to train a baseline classifier and how to do it (this included the basic steps of training using `scikit-learn`)\n",
    "- Decision trees\n",
    "- Parameters and hyperparameters\n",
    "- Decision boundary\n",
    "\n",
    "Let's check your understanding with a Zoom poll."
   ]
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    "<br><br><br><br>"
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    "## Pre-lecture videos\n",
    "\n",
    "You were asked to watch 2 videos before coming to class, one on [generalization](https://youtu.be/iS2hsRRlc2M), one on [data splitting](https://youtu.be/h2AEobwcUQw).\n",
    "\n",
    "Let's test your understanding before we continue."
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    "## Generalization - check-in\n",
    "\n",
    "Generalization refers to the ability of a ML model to correctly classify new, unseen examples.\n",
    "\n",
    "Imagine the following scenario: \n",
    "- We want to predict the color of the shirt a student in this class is wearing.\n",
    "- We collect weight and height of each student and use them as predictors for a decision tree.\n",
    "- The accuracy on the training set (this class) is very high!\n",
    "- Do you think this model will generalize well? [Answer here](https://www.menti.com/yqntes2d6v)."
   ]
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    "## Generalization - check-in\n",
    "\n",
    "The model described above may very well come up with enough rules to associate each student to their shirt's color, but it is unlikely that it will be able to predict new samples (there is no strong correlation between predictors and target).\n",
    "\n",
    "This is an extreme example, but we'll see often cases of ML models learning \"too much\" - rules that apply only to a few samples in the training set.\n",
    "\n",
    "Hyperparameters help us regulate how an algorithm learns."
   ]
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   "source": [
    "## Data Splitting - check-in\n",
    "\n",
    "Looking at the prediction accuracy on the training set will not give us a good estimate of what the error will be on new samples. That's why we normally split the original set and set aside a few samples for testing.\n",
    "\n",
    "Let's answer a few questions: https://www.menti.com/bxfcefbp56"
   ]
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   "source": [
    "The next cells cover all information and code used in the videos, for your own reference."
   ]
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   "source": [
    "## Generalization [[video](https://youtu.be/iS2hsRRlc2M)]"
   ]
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    "### Big picture and motivation \n",
    "\n",
    "In machine learning, we want to glean information from labeled data so that we can label **new unlabeled** data. For example, suppose we want to build a spam filtering system.  We will take a large number of spam/non-spam messages from the past, learn patterns associated with spam/non-spam from them, and predict whether **a new incoming message** in someone's inbox is spam or non-spam based on these patterns. \n",
    "\n",
    "So we want to learn from the past but ultimately we want to apply it on the future email messages. "
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    "![](img/eva-think.png)\n",
    "\n",
    "**How can we generalize from what we've seen to what we haven't seen?** \n",
    "\n",
    "In this lecture, we'll see how machine learning tackles this question. "
   ]
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    "### Model complexity and training error\n",
    "\n",
    "In the last lecture, we looked at decision boundaries, a way to visualize what sort of examples will be classified as positive and negative. \n",
    "\n",
    "Let's examine how does the decision boundary change for different tree depths. "
   ]
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       "      <th></th>\n",
       "      <th>ml_experience</th>\n",
       "      <th>class_attendance</th>\n",
       "      <th>lab1</th>\n",
       "      <th>lab2</th>\n",
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       "      <td>A+</td>\n",
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       "      <td>73</td>\n",
       "      <td>68</td>\n",
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       "      <td>71</td>\n",
       "      <td>not A+</td>\n",
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       "      <td>93</td>\n",
       "      <td>69</td>\n",
       "      <td>79</td>\n",
       "      <td>75</td>\n",
       "      <td>not A+</td>\n",
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       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "      <td>90</td>\n",
       "      <td>94</td>\n",
       "      <td>99</td>\n",
       "      <td>80</td>\n",
       "      <td>not A+</td>\n",
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       "      <td>not A+</td>\n",
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       "   ml_experience  class_attendance  lab1  lab2  lab3  lab4  quiz1   quiz2\n",
       "0              1                 1    92    93    84    91     92      A+\n",
       "1              1                 0    94    90    80    83     91  not A+\n",
       "2              0                 0    78    85    83    80     80  not A+\n",
       "3              0                 1    91    94    92    91     89      A+\n",
       "4              0                 1    77    83    90    92     85      A+\n",
       "5              1                 0    70    73    68    74     71  not A+\n",
       "6              1                 0    80    88    89    88     91      A+\n",
       "7              0                 1    95    93    69    79     75  not A+\n",
       "8              0                 0    97    90    94    99     80  not A+\n",
       "9              1                 1    95    95    94    94     85  not A+"
      ]
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   "source": [
    "# Toy quiz2 grade data\n",
    "classification_df = pd.read_csv(\"data/quiz2-grade-toy-classification.csv\")\n",
    "classification_df.head(10)"
   ]
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   "execution_count": 3,
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   "source": [
    "X = classification_df.drop([\"quiz2\"], axis=1)\n",
    "y = classification_df[\"quiz2\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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      "text/plain": [
       "   lab4  quiz1\n",
       "0    91     92\n",
       "1    83     91\n",
       "2    80     80\n",
       "3    91     89\n",
       "4    92     85"
      ]
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   "source": [
    "X_subset = X[[\"lab4\", \"quiz1\"]]  # Let's consider a subset of the data for visualization\n",
    "X_subset.head()"
   ]
  },
  {
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   "metadata": {
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   "source": [
    "In the following model, this decision boundary is created by asking one question. "
   ]
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   "execution_count": 5,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error:   0.286\n"
     ]
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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "depth = 1\n",
    "model = DecisionTreeClassifier(max_depth=depth)\n",
    "model.fit(X_subset, y)\n",
    "model.score(X_subset, y)\n",
    "print(\"Error:   %0.3f\" % (1 - model.score(X_subset, y)))\n",
    "plot_tree_decision_boundary_and_tree(\n",
    "    model, X_subset, y, x_label=\"lab4\", y_label=\"quiz1\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "In the following model, this decision boundary is created by asking two questions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error:   0.190\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "depth = 2\n",
    "model = DecisionTreeClassifier(max_depth=depth)\n",
    "model.fit(X_subset, y)\n",
    "model.score(X_subset, y)\n",
    "print(\"Error:   %0.3f\" % (1 - model.score(X_subset, y)))\n",
    "plot_tree_decision_boundary_and_tree(\n",
    "    model, X_subset, y, x_label=\"lab4\", y_label=\"quiz1\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Let's look at the decision boundary with depth = 4. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error:   0.048\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "depth = 4\n",
    "model = DecisionTreeClassifier(max_depth=depth)\n",
    "model.fit(X_subset, y)\n",
    "model.score(X_subset, y)\n",
    "print(\"Error:   %0.3f\" % (1 - model.score(X_subset, y)))\n",
    "plot_tree_decision_boundary_and_tree(\n",
    "    model, X_subset, y, x_label=\"lab4\", y_label=\"quiz1\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Let's look at the decision boundary with depth = 6. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error:   0.000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "depth = 6\n",
    "model = DecisionTreeClassifier(max_depth=depth)\n",
    "model.fit(X_subset, y)\n",
    "model.score(X_subset, y)\n",
    "print(\"Error:   %0.3f\" % (1 - model.score(X_subset, y)))\n",
    "plot_tree_decision_boundary_and_tree(\n",
    "    model, X_subset, y, x_label=\"lab4\", y_label=\"quiz1\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_depths = np.arange(1, 18)\n",
    "errors = []\n",
    "for max_depth in max_depths:\n",
    "    error = 1 - DecisionTreeClassifier(max_depth=max_depth).fit(X_subset, y).score(\n",
    "        X_subset, y\n",
    "    )\n",
    "    errors.append(error)\n",
    "plt.plot(max_depths, errors)\n",
    "plt.xlabel(\"max depth\")\n",
    "plt.ylabel(\"error\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "- Our model has 0% error for depths >= 6!! \n",
    "- But it's also becoming more and more specific and sensitive to the training data.  \n",
    "- Is it good or bad?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "```{admonition} Note \n",
    ":class: note\n",
    "Although the plot above (complexity hyperparameter vs error) is more popular, we could also look at the same plot flip the $y$-axis, i.e., consider accuracy instead of error. \n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_depths = np.arange(1, 18)\n",
    "accuracies = []\n",
    "for max_depth in max_depths:\n",
    "    accuracy = (\n",
    "        DecisionTreeClassifier(max_depth=max_depth).fit(X_subset, y).score(X_subset, y)\n",
    "    )\n",
    "    accuracies.append(accuracy)\n",
    "plt.plot(max_depths, accuracies)\n",
    "plt.xlabel(\"max depth\")\n",
    "plt.ylabel(\"accuracy\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### 🤔 Eva's questions\n",
    "\n",
    "![](img/eva-think.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "At this point Eva is wondering about the following questions. \n",
    "\n",
    "- How to pick the best depth? \n",
    "- How can we make sure that the model we have built would do reasonably well on new data in the wild when it's deployed? \n",
    "- Which of the following rules learned by the decision tree algorithm are likely to generalize better to new data? \n",
    "\n",
    "> Rule 1: If class_attendance == 1 then grade is A+. \n",
    "\n",
    "> Rule 2: If lab3 > 83.5 and quiz1 <= 83.5 and lab2 <= 88 then quiz2 grade is A+\n",
    "\n",
    "To better understand the material in the next sections, think about these questions on your own or discuss them with your friend/neighbour before proceeding. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Generalization: Fundamental goal of ML\n",
    "\n",
    "> **To generalize beyond what we see in the training examples**\n",
    "\n",
    "We only have access to limited amount of training data and we want to learn a mapping function which would predict targets reasonably well for examples beyond this training data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "- Example: Imagine that a learner sees the following images and corresponding labels. \n",
    "\n",
    "![](img/generalization-train.png)\n",
    "<!-- <center>\n",
    "<img src='img/generalization-train.png' width=\"600\" height=\"600\" />\n",
    "</center>     -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Generalizing to unseen data\n",
    "\n",
    "- Now the learner is presented with new images (1 to 4) for prediction. \n",
    "- What prediction would you expect for each image?   \n",
    "\n",
    "![](img/generalization-predict.png)\n",
    "\n",
    "<!-- <center>\n",
    "<img src='img/generalization-predict.png' width=\"1000\" height=\"1000\" />\n",
    "</center>     -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "- Goal: We want the learner to be able to generalize beyond what it has seen in the training data.\n",
    "- But these new examples should be representative of the training data. That is they should have the same characteristics as the training data. \n",
    "- In this example, we would like the leaner to be able to predict labels for test examples 1, 2, and 3 accurately. Although 2, 3 don't exactly occur in the training data, they are very much similar to the images in the training data. That said, is it fair to expect the learner to label image 4 correctly? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Training error vs. Generalization error \n",
    "\n",
    "- Given a model $M$, in ML, people usually talk about two kinds of errors of $M$. \n",
    "    1. Error on the training data: $error_{training}(M)$ \n",
    "    2. Error on the entire distribution $D$ of data: $error_{D}(M)$\n",
    "- We are interested in the error on the entire distribution     \n",
    "    - ... But we do not have access to the entire distribution 😞"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "## Data Splitting [[video](https://youtu.be/h2AEobwcUQw)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### How to approximate generalization error? \n",
    "\n",
    "A common way is **data splitting**. \n",
    "- Keep aside some randomly selected portion from the training data.\n",
    "- `fit` (train) a model on the training portion only. \n",
    "- `score` (assess) the trained model on this set aside data to get a sense of how well the model would be able to generalize.\n",
    "- Pretend that the kept aside data is representative of the real distribution $D$ of data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "![](img/eva-good-idea.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kvarada/opt/miniconda3/envs/cpsc330/lib/python3.9/site-packages/IPython/core/display.py:724: UserWarning: Consider using IPython.display.IFrame instead\n",
      "  warnings.warn(\"Consider using IPython.display.IFrame instead\")\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe src=https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html width=1000 height=800></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# scikit-learn train_test_split\n",
    "url = \"https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html\"\n",
    "HTML(\"<iframe src=%s width=1000 height=800></iframe>\" % url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "- We can pass `X` and `y` or a dataframe with both `X` and `y` in it. \n",
    "- We can also specify the train or test split sizes. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "### Simple train/test split \n",
    "\n",
    "- The picture shows an 80%-20% split of a toy dataset with 10 examples. \n",
    "- The data is shuffled before splitting. \n",
    "- Usually when we do machine learning we split the data before doing anything and put the test data in an imaginary chest lock. \n",
    "\n",
    "![](img/train-test-split.png)\n",
    "\n",
    "<!-- <img src='img/train-test-split.png' width=\"1500\" height=\"1500\" /> -->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# Let's demonstrate this with the canada usa cities data\n",
    "# The data is available in the data directory\n",
    "df = pd.read_csv(\"data/canada_usa_cities.csv\")\n",
    "X = df.drop(columns=[\"country\"])\n",
    "y = df[\"country\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-130.0437</td>\n",
       "      <td>55.9773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-134.4197</td>\n",
       "      <td>58.3019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-123.0780</td>\n",
       "      <td>48.9854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.7436</td>\n",
       "      <td>48.9881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.2691</td>\n",
       "      <td>48.9951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>-72.7218</td>\n",
       "      <td>45.3990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>-66.6458</td>\n",
       "      <td>45.9664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>-79.2506</td>\n",
       "      <td>42.9931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>-72.9406</td>\n",
       "      <td>45.6275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>-79.4608</td>\n",
       "      <td>46.3092</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>209 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     longitude  latitude\n",
       "0    -130.0437   55.9773\n",
       "1    -134.4197   58.3019\n",
       "2    -123.0780   48.9854\n",
       "3    -122.7436   48.9881\n",
       "4    -122.2691   48.9951\n",
       "..         ...       ...\n",
       "204   -72.7218   45.3990\n",
       "205   -66.6458   45.9664\n",
       "206   -79.2506   42.9931\n",
       "207   -72.9406   45.6275\n",
       "208   -79.4608   46.3092\n",
       "\n",
       "[209 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         USA\n",
       "1         USA\n",
       "2         USA\n",
       "3         USA\n",
       "4         USA\n",
       "        ...  \n",
       "204    Canada\n",
       "205    Canada\n",
       "206    Canada\n",
       "207    Canada\n",
       "208    Canada\n",
       "Name: country, Length: 209, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Data portion</th>\n",
       "      <th>Shape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>X</td>\n",
       "      <td>(209, 2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>y</td>\n",
       "      <td>(209,)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>X_train</td>\n",
       "      <td>(167, 2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>y_train</td>\n",
       "      <td>(167,)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>X_test</td>\n",
       "      <td>(42, 2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>y_test</td>\n",
       "      <td>(42,)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=123\n",
    ")  # 80%-20% train test split on X and y\n",
    "\n",
    "# Print shapes\n",
    "shape_dict = {\n",
    "    \"Data portion\": [\"X\", \"y\", \"X_train\", \"y_train\", \"X_test\", \"y_test\"],\n",
    "    \"Shape\": [\n",
    "        X.shape,\n",
    "        y.shape,\n",
    "        X_train.shape,\n",
    "        y_train.shape,\n",
    "        X_test.shape,\n",
    "        y_test.shape,\n",
    "    ],\n",
    "}\n",
    "\n",
    "shape_df = pd.DataFrame(shape_dict)\n",
    "HTML(shape_df.to_html(index=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "#### Creating `train_df` and `test_df`\n",
    "\n",
    "- Sometimes we want to keep the target in the train split for EDA or for visualization. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>-76.4813</td>\n",
       "      <td>44.2307</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>-81.2496</td>\n",
       "      <td>42.9837</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>-66.0580</td>\n",
       "      <td>45.2788</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>-73.2533</td>\n",
       "      <td>45.3057</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>-67.9245</td>\n",
       "      <td>47.1652</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     longitude  latitude country\n",
       "160   -76.4813   44.2307  Canada\n",
       "127   -81.2496   42.9837  Canada\n",
       "169   -66.0580   45.2788  Canada\n",
       "188   -73.2533   45.3057  Canada\n",
       "187   -67.9245   47.1652  Canada"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df, test_df = train_test_split(\n",
    "    df, test_size=0.2, random_state=123\n",
    ")  # 80%-20% train test split on df\n",
    "X_train, y_train = train_df.drop(columns=[\"country\"]), train_df[\"country\"]\n",
    "X_test, y_test = test_df.drop(columns=[\"country\"]), test_df[\"country\"]\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.discrete_scatter(X.iloc[:, 0], X.iloc[:, 1], y, s=12)\n",
    "plt.xlabel(\"longitude\")\n",
    "plt.ylabel(\"latitude\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 2.48.0 (0)\n",
       " -->\n",
       "<!-- Title: Tree Pages: 1 -->\n",
       "<svg width=\"1168pt\" height=\"932pt\"\n",
       " viewBox=\"0.00 0.00 1168.00 932.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 928)\">\n",
       "<title>Tree</title>\n",
       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-928 1164,-928 1164,4 -4,4\"/>\n",
       "<!-- 0 -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>0</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"285.5,-924 179.5,-924 179.5,-886 285.5,-886 285.5,-924\"/>\n",
       "<text text-anchor=\"middle\" x=\"232.5\" y=\"-908.8\" font-family=\"Times,serif\" font-size=\"14.00\">latitude &lt;= 42.9</text>\n",
       "<text text-anchor=\"middle\" x=\"232.5\" y=\"-893.8\" font-family=\"Times,serif\" font-size=\"14.00\">class = Canada</text>\n",
       "</g>\n",
       "<!-- 1 -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>1</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"223.5,-850 103.5,-850 103.5,-812 223.5,-812 223.5,-850\"/>\n",
       "<text text-anchor=\"middle\" x=\"163.5\" y=\"-834.8\" font-family=\"Times,serif\" font-size=\"14.00\">latitude &lt;= 42.096</text>\n",
       "<text text-anchor=\"middle\" x=\"163.5\" y=\"-819.8\" font-family=\"Times,serif\" font-size=\"14.00\">class = USA</text>\n",
       "</g>\n",
       "<!-- 0&#45;&gt;1 -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>0&#45;&gt;1</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M215.09,-885.83C206.98,-877.37 197.18,-867.15 188.32,-857.9\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"190.59,-855.21 181.15,-850.41 185.54,-860.05 190.59,-855.21\"/>\n",
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       "<text text-anchor=\"middle\" x=\"301.5\" y=\"-819.8\" font-family=\"Times,serif\" font-size=\"14.00\">class = Canada</text>\n",
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       "<!-- 0&#45;&gt;6 -->\n",
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       "<text text-anchor=\"middle\" x=\"43.5\" y=\"-753.3\" font-family=\"Times,serif\" font-size=\"14.00\">class = USA</text>\n",
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       "<!-- 1&#45;&gt;2 -->\n",
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       "<text text-anchor=\"middle\" x=\"165.5\" y=\"-760.8\" font-family=\"Times,serif\" font-size=\"14.00\">latitude &lt;= 42.324</text>\n",
       "<text text-anchor=\"middle\" x=\"165.5\" y=\"-745.8\" font-family=\"Times,serif\" font-size=\"14.00\">class = USA</text>\n",
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       "<!-- 1&#45;&gt;3 -->\n",
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       "<text text-anchor=\"middle\" x=\"54.5\" y=\"-679.3\" font-family=\"Times,serif\" font-size=\"14.00\">class = Canada</text>\n",
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       "<!-- 3&#45;&gt;4 -->\n",
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       "<!-- 3&#45;&gt;5 -->\n",
       "<g id=\"edge5\" class=\"edge\">\n",
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       "<text text-anchor=\"middle\" x=\"300.5\" y=\"-760.8\" font-family=\"Times,serif\" font-size=\"14.00\">latitude &lt;= 49.0</text>\n",
       "<text text-anchor=\"middle\" x=\"300.5\" y=\"-745.8\" font-family=\"Times,serif\" font-size=\"14.00\">class = Canada</text>\n",
       "</g>\n",
       "<!-- 6&#45;&gt;7 -->\n",
       "<g id=\"edge7\" class=\"edge\">\n",
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       "<text text-anchor=\"middle\" x=\"415.5\" y=\"-753.3\" font-family=\"Times,serif\" font-size=\"14.00\">class = USA</text>\n",
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       "<!-- 6&#45;&gt;54 -->\n",
       "<g id=\"edge54\" class=\"edge\">\n",
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       "<path fill=\"none\" stroke=\"black\" d=\"M330.27,-811.83C345.29,-802.34 363.82,-790.64 379.76,-780.57\"/>\n",
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       "<title>8</title>\n",
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       "<text text-anchor=\"middle\" x=\"299.5\" y=\"-686.8\" font-family=\"Times,serif\" font-size=\"14.00\">longitude &lt;= &#45;94.582</text>\n",
       "<text text-anchor=\"middle\" x=\"299.5\" y=\"-671.8\" font-family=\"Times,serif\" font-size=\"14.00\">class = Canada</text>\n",
       "</g>\n",
       "<!-- 7&#45;&gt;8 -->\n",
       "<g id=\"edge8\" class=\"edge\">\n",
       "<title>7&#45;&gt;8</title>\n",
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       "<title>53</title>\n",
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       "<text text-anchor=\"middle\" x=\"435.5\" y=\"-679.3\" font-family=\"Times,serif\" font-size=\"14.00\">class = Canada</text>\n",
       "</g>\n",
       "<!-- 7&#45;&gt;53 -->\n",
       "<g id=\"edge53\" class=\"edge\">\n",
       "<title>7&#45;&gt;53</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M334.57,-737.83C352.69,-728.17 375.12,-716.2 394.22,-706.01\"/>\n",
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       "<title>9</title>\n",
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       "<text text-anchor=\"middle\" x=\"221.5\" y=\"-612.8\" font-family=\"Times,serif\" font-size=\"14.00\">longitude &lt;= &#45;123.222</text>\n",
       "<text text-anchor=\"middle\" x=\"221.5\" y=\"-597.8\" font-family=\"Times,serif\" font-size=\"14.00\">class = USA</text>\n",
       "</g>\n",
       "<!-- 8&#45;&gt;9 -->\n",
       "<g id=\"edge9\" class=\"edge\">\n",
       "<title>8&#45;&gt;9</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M279.82,-663.83C270.39,-655.13 258.96,-644.58 248.72,-635.13\"/>\n",
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       "<title>12</title>\n",
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       "<text text-anchor=\"middle\" x=\"378.5\" y=\"-597.8\" font-family=\"Times,serif\" font-size=\"14.00\">class = Canada</text>\n",
       "</g>\n",
       "<!-- 8&#45;&gt;12 -->\n",
       "<g id=\"edge12\" class=\"edge\">\n",
       "<title>8&#45;&gt;12</title>\n",
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       "<title>10</title>\n",
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       "<text text-anchor=\"middle\" x=\"118.5\" y=\"-531.3\" font-family=\"Times,serif\" font-size=\"14.00\">class = Canada</text>\n",
       "</g>\n",
       "<!-- 9&#45;&gt;10 -->\n",
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       "<title>9&#45;&gt;10</title>\n",
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       "<title>11</title>\n",
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       "<text text-anchor=\"middle\" x=\"230.5\" y=\"-531.3\" font-family=\"Times,serif\" font-size=\"14.00\">class = USA</text>\n",
       "</g>\n",
       "<!-- 9&#45;&gt;11 -->\n",
       "<g id=\"edge11\" class=\"edge\">\n",
       "<title>9&#45;&gt;11</title>\n",
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       "<title>13</title>\n",
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       "<text text-anchor=\"middle\" x=\"369.5\" y=\"-538.8\" font-family=\"Times,serif\" font-size=\"14.00\">latitude &lt;= 43.093</text>\n",
       "<text text-anchor=\"middle\" x=\"369.5\" y=\"-523.8\" font-family=\"Times,serif\" font-size=\"14.00\">class = Canada</text>\n",
       "</g>\n",
       "<!-- 12&#45;&gt;13 -->\n",
       "<g id=\"edge13\" class=\"edge\">\n",
       "<title>12&#45;&gt;13</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M376.23,-589.83C375.27,-582.13 374.12,-572.97 373.05,-564.42\"/>\n",
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       "<title>36</title>\n",
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       "<text text-anchor=\"middle\" x=\"609.5\" y=\"-523.8\" font-family=\"Times,serif\" font-size=\"14.00\">class = Canada</text>\n",
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       "<!-- 12&#45;&gt;36 -->\n",
       "<g id=\"edge36\" class=\"edge\">\n",
       "<title>12&#45;&gt;36</title>\n",
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       "<title>14</title>\n",
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       "<text text-anchor=\"middle\" x=\"223.5\" y=\"-464.8\" font-family=\"Times,serif\" font-size=\"14.00\">longitude &lt;= &#45;81.845</text>\n",
       "<text text-anchor=\"middle\" x=\"223.5\" y=\"-449.8\" font-family=\"Times,serif\" font-size=\"14.00\">class = Canada</text>\n",
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       "<!-- 13&#45;&gt;14 -->\n",
       "<g id=\"edge14\" class=\"edge\">\n",
       "<title>13&#45;&gt;14</title>\n",
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       "<text text-anchor=\"middle\" x=\"369.5\" y=\"-449.8\" font-family=\"Times,serif\" font-size=\"14.00\">class = Canada</text>\n",
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       "<!-- 13&#45;&gt;19 -->\n",
       "<g id=\"edge19\" class=\"edge\">\n",
       "<title>13&#45;&gt;19</title>\n",
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       "<title>15</title>\n",
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       "<text text-anchor=\"middle\" x=\"101.5\" y=\"-383.3\" font-family=\"Times,serif\" font-size=\"14.00\">class = USA</text>\n",
       "</g>\n",
       "<!-- 14&#45;&gt;15 -->\n",
       "<g id=\"edge15\" class=\"edge\">\n",
       "<title>14&#45;&gt;15</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M192.71,-441.83C176.48,-432.25 156.44,-420.42 139.27,-410.29\"/>\n",
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       "<text text-anchor=\"middle\" x=\"223.5\" y=\"-375.8\" font-family=\"Times,serif\" font-size=\"14.00\">class = Canada</text>\n",
       "</g>\n",
       "<!-- 14&#45;&gt;16 -->\n",
       "<g id=\"edge16\" class=\"edge\">\n",
       "<title>14&#45;&gt;16</title>\n",
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       "<!-- 16&#45;&gt;17 -->\n",
       "<g id=\"edge17\" class=\"edge\">\n",
       "<title>16&#45;&gt;17</title>\n",
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       "<!-- 16&#45;&gt;18 -->\n",
       "<g id=\"edge18\" class=\"edge\">\n",
       "<title>16&#45;&gt;18</title>\n",
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       "<title>20&#45;&gt;21</title>\n",
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       "<!-- 30 -->\n",
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       "<title>30</title>\n",
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       "<text text-anchor=\"middle\" x=\"498.5\" y=\"-316.8\" font-family=\"Times,serif\" font-size=\"14.00\">latitude &lt;= 44.706</text>\n",
       "<text text-anchor=\"middle\" x=\"498.5\" y=\"-301.8\" font-family=\"Times,serif\" font-size=\"14.00\">class = USA</text>\n",
       "</g>\n",
       "<!-- 20&#45;&gt;30 -->\n",
       "<g id=\"edge30\" class=\"edge\">\n",
       "<title>20&#45;&gt;30</title>\n",
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       "<title>22</title>\n",
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       "<text text-anchor=\"middle\" x=\"223.5\" y=\"-235.3\" font-family=\"Times,serif\" font-size=\"14.00\">class = Canada</text>\n",
       "</g>\n",
       "<!-- 21&#45;&gt;22 -->\n",
       "<g id=\"edge22\" class=\"edge\">\n",
       "<title>21&#45;&gt;22</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M319.95,-293.83C302.79,-284.25 281.59,-272.42 263.44,-262.29\"/>\n",
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       "<title>23</title>\n",
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       "<text text-anchor=\"middle\" x=\"352.5\" y=\"-242.8\" font-family=\"Times,serif\" font-size=\"14.00\">latitude &lt;= 44.168</text>\n",
       "<text text-anchor=\"middle\" x=\"352.5\" y=\"-227.8\" font-family=\"Times,serif\" font-size=\"14.00\">class = Canada</text>\n",
       "</g>\n",
       "<!-- 21&#45;&gt;23 -->\n",
       "<g id=\"edge23\" class=\"edge\">\n",
       "<title>21&#45;&gt;23</title>\n",
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       "<!-- 24 -->\n",
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       "<title>24</title>\n",
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       "<text text-anchor=\"middle\" x=\"230.5\" y=\"-168.8\" font-family=\"Times,serif\" font-size=\"14.00\">latitude &lt;= 43.511</text>\n",
       "<text text-anchor=\"middle\" x=\"230.5\" y=\"-153.8\" font-family=\"Times,serif\" font-size=\"14.00\">class = USA</text>\n",
       "</g>\n",
       "<!-- 23&#45;&gt;24 -->\n",
       "<g id=\"edge24\" class=\"edge\">\n",
       "<title>23&#45;&gt;24</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M321.71,-219.83C306.11,-210.62 286.98,-199.33 270.27,-189.47\"/>\n",
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       "<title>29</title>\n",
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       "<text text-anchor=\"middle\" x=\"358.5\" y=\"-161.3\" font-family=\"Times,serif\" font-size=\"14.00\">class = Canada</text>\n",
       "</g>\n",
       "<!-- 23&#45;&gt;29 -->\n",
       "<g id=\"edge29\" class=\"edge\">\n",
       "<title>23&#45;&gt;29</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M354.01,-219.83C354.68,-211.89 355.47,-202.41 356.2,-193.63\"/>\n",
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       "<title>25</title>\n",
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       "<text text-anchor=\"middle\" x=\"169.5\" y=\"-87.3\" font-family=\"Times,serif\" font-size=\"14.00\">class = USA</text>\n",
       "</g>\n",
       "<!-- 24&#45;&gt;25 -->\n",
       "<g id=\"edge25\" class=\"edge\">\n",
       "<title>24&#45;&gt;25</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M215.11,-145.83C207.79,-137.2 198.92,-126.73 190.96,-117.33\"/>\n",
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       "<!-- 26 -->\n",
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       "<title>26</title>\n",
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       "<text text-anchor=\"middle\" x=\"291.5\" y=\"-94.8\" font-family=\"Times,serif\" font-size=\"14.00\">latitude &lt;= 43.988</text>\n",
       "<text text-anchor=\"middle\" x=\"291.5\" y=\"-79.8\" font-family=\"Times,serif\" font-size=\"14.00\">class = Canada</text>\n",
       "</g>\n",
       "<!-- 24&#45;&gt;26 -->\n",
       "<g id=\"edge26\" class=\"edge\">\n",
       "<title>24&#45;&gt;26</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M245.89,-145.83C252.99,-137.45 261.55,-127.35 269.32,-118.17\"/>\n",
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       "<title>27</title>\n",
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       "<text text-anchor=\"middle\" x=\"235.5\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">class = Canada</text>\n",
       "</g>\n",
       "<!-- 26&#45;&gt;27 -->\n",
       "<g id=\"edge27\" class=\"edge\">\n",
       "<title>26&#45;&gt;27</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M277.08,-71.72C270.43,-63.29 262.43,-53.15 255.23,-44.02\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"257.85,-41.69 248.91,-36 252.36,-46.02 257.85,-41.69\"/>\n",
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       "<!-- 28 -->\n",
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       "<title>28</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"391,-36 304,-36 304,0 391,0 391,-36\"/>\n",
       "<text text-anchor=\"middle\" x=\"347.5\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">class = USA</text>\n",
       "</g>\n",
       "<!-- 26&#45;&gt;28 -->\n",
       "<g id=\"edge28\" class=\"edge\">\n",
       "<title>26&#45;&gt;28</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M305.92,-71.72C312.57,-63.29 320.57,-53.15 327.77,-44.02\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"330.64,-46.02 334.09,-36 325.15,-41.69 330.64,-46.02\"/>\n",
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       "<!-- 31 -->\n",
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       "<title>31</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"518,-257 431,-257 431,-221 518,-221 518,-257\"/>\n",
       "<text text-anchor=\"middle\" x=\"474.5\" y=\"-235.3\" font-family=\"Times,serif\" font-size=\"14.00\">class = USA</text>\n",
       "</g>\n",
       "<!-- 30&#45;&gt;31 -->\n",
       "<g id=\"edge31\" class=\"edge\">\n",
       "<title>30&#45;&gt;31</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M492.44,-293.83C489.74,-285.72 486.5,-275.99 483.52,-267.05\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"486.79,-265.8 480.31,-257.42 480.15,-268.01 486.79,-265.8\"/>\n",
       "</g>\n",
       "<!-- 32 -->\n",
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       "<title>32</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"656.5,-258 536.5,-258 536.5,-220 656.5,-220 656.5,-258\"/>\n",
       "<text text-anchor=\"middle\" x=\"596.5\" y=\"-242.8\" font-family=\"Times,serif\" font-size=\"14.00\">latitude &lt;= 44.848</text>\n",
       "<text text-anchor=\"middle\" x=\"596.5\" y=\"-227.8\" font-family=\"Times,serif\" font-size=\"14.00\">class = Canada</text>\n",
       "</g>\n",
       "<!-- 30&#45;&gt;32 -->\n",
       "<g id=\"edge32\" class=\"edge\">\n",
       "<title>30&#45;&gt;32</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M523.23,-293.83C535.42,-284.88 550.28,-273.96 563.43,-264.3\"/>\n",
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       "<title>33</title>\n",
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       "<text text-anchor=\"middle\" x=\"484.5\" y=\"-161.3\" font-family=\"Times,serif\" font-size=\"14.00\">class = Canada</text>\n",
       "</g>\n",
       "<!-- 32&#45;&gt;33 -->\n",
       "<g id=\"edge33\" class=\"edge\">\n",
       "<title>32&#45;&gt;33</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M568.24,-219.83C553.61,-210.43 535.61,-198.86 520.05,-188.85\"/>\n",
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       "<title>34</title>\n",
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       "<text text-anchor=\"middle\" x=\"596.5\" y=\"-161.3\" font-family=\"Times,serif\" font-size=\"14.00\">class = USA</text>\n",
       "</g>\n",
       "<!-- 32&#45;&gt;34 -->\n",
       "<g id=\"edge34\" class=\"edge\">\n",
       "<title>32&#45;&gt;34</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M596.5,-219.83C596.5,-211.89 596.5,-202.41 596.5,-193.63\"/>\n",
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       "<!-- 37 -->\n",
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       "<title>37</title>\n",
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       "<text text-anchor=\"middle\" x=\"609.5\" y=\"-464.8\" font-family=\"Times,serif\" font-size=\"14.00\">latitude &lt;= 45.195</text>\n",
       "<text text-anchor=\"middle\" x=\"609.5\" y=\"-449.8\" font-family=\"Times,serif\" font-size=\"14.00\">class = USA</text>\n",
       "</g>\n",
       "<!-- 36&#45;&gt;37 -->\n",
       "<g id=\"edge37\" class=\"edge\">\n",
       "<title>36&#45;&gt;37</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M609.5,-515.83C609.5,-508.13 609.5,-498.97 609.5,-490.42\"/>\n",
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       "<title>52</title>\n",
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       "<text text-anchor=\"middle\" x=\"738.5\" y=\"-457.3\" font-family=\"Times,serif\" font-size=\"14.00\">class = Canada</text>\n",
       "</g>\n",
       "<!-- 36&#45;&gt;52 -->\n",
       "<g id=\"edge52\" class=\"edge\">\n",
       "<title>36&#45;&gt;52</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M642.05,-515.83C659.21,-506.25 680.41,-494.42 698.56,-484.29\"/>\n",
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       "<title>38</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"653,-405 566,-405 566,-369 653,-369 653,-405\"/>\n",
       "<text text-anchor=\"middle\" x=\"609.5\" y=\"-383.3\" font-family=\"Times,serif\" font-size=\"14.00\">class = USA</text>\n",
       "</g>\n",
       "<!-- 37&#45;&gt;38 -->\n",
       "<g id=\"edge38\" class=\"edge\">\n",
       "<title>37&#45;&gt;38</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M609.5,-441.83C609.5,-433.89 609.5,-424.41 609.5,-415.63\"/>\n",
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       "<!-- 39 -->\n",
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       "<title>39</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"799.5,-406 671.5,-406 671.5,-368 799.5,-368 799.5,-406\"/>\n",
       "<text text-anchor=\"middle\" x=\"735.5\" y=\"-390.8\" font-family=\"Times,serif\" font-size=\"14.00\">longitude &lt;= &#45;67.93</text>\n",
       "<text text-anchor=\"middle\" x=\"735.5\" y=\"-375.8\" font-family=\"Times,serif\" font-size=\"14.00\">class = Canada</text>\n",
       "</g>\n",
       "<!-- 37&#45;&gt;39 -->\n",
       "<g id=\"edge39\" class=\"edge\">\n",
       "<title>37&#45;&gt;39</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M641.3,-441.83C657.56,-432.54 677.54,-421.12 694.91,-411.2\"/>\n",
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       "<!-- 40 -->\n",
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       "<title>40</title>\n",
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       "<text text-anchor=\"middle\" x=\"735.5\" y=\"-316.8\" font-family=\"Times,serif\" font-size=\"14.00\">longitude &lt;= &#45;68.325</text>\n",
       "<text text-anchor=\"middle\" x=\"735.5\" y=\"-301.8\" font-family=\"Times,serif\" font-size=\"14.00\">class = USA</text>\n",
       "</g>\n",
       "<!-- 39&#45;&gt;40 -->\n",
       "<g id=\"edge40\" class=\"edge\">\n",
       "<title>39&#45;&gt;40</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M735.5,-367.83C735.5,-360.13 735.5,-350.97 735.5,-342.42\"/>\n",
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       "<title>47</title>\n",
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       "<text text-anchor=\"middle\" x=\"980.5\" y=\"-316.8\" font-family=\"Times,serif\" font-size=\"14.00\">latitude &lt;= 45.565</text>\n",
       "<text text-anchor=\"middle\" x=\"980.5\" y=\"-301.8\" font-family=\"Times,serif\" font-size=\"14.00\">class = Canada</text>\n",
       "</g>\n",
       "<!-- 39&#45;&gt;47 -->\n",
       "<g id=\"edge47\" class=\"edge\">\n",
       "<title>39&#45;&gt;47</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M797.01,-367.92C831.47,-357.8 874.58,-345.13 910.36,-334.61\"/>\n",
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       "<title>41</title>\n",
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       "<text text-anchor=\"middle\" x=\"735.5\" y=\"-242.8\" font-family=\"Times,serif\" font-size=\"14.00\">latitude &lt;= 47.256</text>\n",
       "<text text-anchor=\"middle\" x=\"735.5\" y=\"-227.8\" font-family=\"Times,serif\" font-size=\"14.00\">class = Canada</text>\n",
       "</g>\n",
       "<!-- 40&#45;&gt;41 -->\n",
       "<g id=\"edge41\" class=\"edge\">\n",
       "<title>40&#45;&gt;41</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M735.5,-293.83C735.5,-286.13 735.5,-276.97 735.5,-268.42\"/>\n",
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       "<title>46</title>\n",
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       "<text text-anchor=\"middle\" x=\"857.5\" y=\"-235.3\" font-family=\"Times,serif\" font-size=\"14.00\">class = USA</text>\n",
       "</g>\n",
       "<!-- 40&#45;&gt;46 -->\n",
       "<g id=\"edge46\" class=\"edge\">\n",
       "<title>40&#45;&gt;46</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M766.29,-293.83C782.52,-284.25 802.56,-272.42 819.73,-262.29\"/>\n",
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       "<!-- 42 -->\n",
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       "<title>42</title>\n",
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       "<text text-anchor=\"middle\" x=\"708.5\" y=\"-161.3\" font-family=\"Times,serif\" font-size=\"14.00\">class = Canada</text>\n",
       "</g>\n",
       "<!-- 41&#45;&gt;42 -->\n",
       "<g id=\"edge42\" class=\"edge\">\n",
       "<title>41&#45;&gt;42</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M728.69,-219.83C725.64,-211.72 722,-201.99 718.64,-193.05\"/>\n",
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       "<title>43</title>\n",
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       "<text text-anchor=\"middle\" x=\"844.5\" y=\"-153.8\" font-family=\"Times,serif\" font-size=\"14.00\">class = USA</text>\n",
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       "<!-- 41&#45;&gt;43 -->\n",
       "<g id=\"edge43\" class=\"edge\">\n",
       "<title>41&#45;&gt;43</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M763.01,-219.83C776.82,-210.71 793.73,-199.54 808.55,-189.74\"/>\n",
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       "<title>44</title>\n",
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       "<!-- 43&#45;&gt;44 -->\n",
       "<g id=\"edge44\" class=\"edge\">\n",
       "<title>43&#45;&gt;44</title>\n",
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       "</g>\n",
       "<!-- 48&#45;&gt;49 -->\n",
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       "<title>48&#45;&gt;49</title>\n",
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       "<text text-anchor=\"middle\" x=\"1092.5\" y=\"-161.3\" font-family=\"Times,serif\" font-size=\"14.00\">class = USA</text>\n",
       "</g>\n",
       "<!-- 48&#45;&gt;50 -->\n",
       "<g id=\"edge50\" class=\"edge\">\n",
       "<title>48&#45;&gt;50</title>\n",
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      ],
      "text/plain": [
       "<graphviz.files.Source at 0x12e36daf0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = DecisionTreeClassifier()\n",
    "model.fit(X_train, y_train)\n",
    "display_tree(X_train.columns, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Let's examine the train and test accuracies with the split now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy:   1.000\n",
      "Test accuracy:   0.738\n"
     ]
    }
   ],
   "source": [
    "print(\"Train accuracy:   %0.3f\" % model.score(X_train, y_train))\n",
    "print(\"Test accuracy:   %0.3f\" % model.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_tree_decision_boundary_and_tree(model, X, y, height=6, width=16, eps=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(16, 6), subplot_kw={\"xticks\": (), \"yticks\": ()})\n",
    "plot_tree_decision_boundary(\n",
    "    model,\n",
    "    X_train,\n",
    "    y_train,\n",
    "    eps=10,\n",
    "    x_label=\"longitude\",\n",
    "    y_label=\"latitude\",\n",
    "    ax=ax[0],\n",
    "    title=\"Decision tree model on the train data\",\n",
    ")\n",
    "plot_tree_decision_boundary(\n",
    "    model,\n",
    "    X_test,\n",
    "    y_test,\n",
    "    eps=10,\n",
    "    x_label=\"longitude\",\n",
    "    y_label=\"latitude\",\n",
    "    ax=ax[1],\n",
    "    title=\"Decision tree model on the test data\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "- Useful arguments of `train_test_split`: \n",
    "    - `test_size`\n",
    "    - `train_size` \n",
    "    - `random_state`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "#### `test_size`, `train_size` arguments\n",
    "\n",
    "- Let's us specify how we want to split the data. \n",
    "- We can specify either of the two. See the documentation [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html).\n",
    "- There is no hard and fast rule on what split sizes should we use. \n",
    "    - It depends upon how much data is available to you. \n",
    "- Some common splits are 90/10, 80/20, 70/30 (training/test).\n",
    "- In the above example, we used 80/20 split. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "#### `random_state` argument\n",
    "\n",
    "- The data is shuffled before splitting which is crucial step. (You will explore this in the lab.) \n",
    "- The `random_state` argument controls this shuffling. \n",
    "- In the example above we used `random_state=123`. If you run this notebook with the same `random_state` it should give you exactly the same split. \n",
    "    - Useful when you want reproducible results. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Train/validation/test split\n",
    "\n",
    "- Some of you may have heard of \"validation\" data.\n",
    "- Sometimes it's a good idea to have a separate data for hyperparameter tuning. \n",
    "\n",
    "![](img/train-valid-test-split.png)\n",
    "\n",
    "<!-- <img src='img/train-valid-test-split.png' width=\"1500\" height=\"1500\" /> -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "- We will try to use \"validation\" to refer to data where we have access to the target values.\n",
    "  - But, unlike the training data, we only use this for hyperparameter tuning and model assessment; we don't pass these into `fit`.  \n",
    "- We will try to use \"test\" to refer to data where we have access to the target values \n",
    "  - But, unlike training and validation data, we neither use it in training nor hyperparameter optimization. \n",
    "  - We only use it **once** to evaluate the performance of the best performing model on the validation set.   \n",
    "  - We lock it in a \"vault\" until we're ready to evaluate. \n",
    "\n",
    "Note that there isn't good concensus on the terminology of what is validation and what is test. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "```{admonition} Note \n",
    ":class: note\n",
    "Validation data is also referred to as **development data** or **dev set** for short.  \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### \"Deployment\" data\n",
    "\n",
    "- After we build and finalize a model, we deploy it, and then the model deals with the data in the wild. \n",
    "- We will use \"deployment\" to refer to this data, where we do **not** have access to the target values.\n",
    "- Deployment error is what we _really_ care about.\n",
    "- We use validation and test errors as proxies for deployment error, and we hope they are similar.\n",
    "- So, if our model does well on the validation and test data, we hope it will do well on deployment data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Summary of train, validation, test, and deployment data\n",
    "\n",
    "|         | `fit` | `score` | `predict` |\n",
    "|----------|-------|---------|-----------|\n",
    "| Train    | ✔️      | ✔️      | ✔️         |\n",
    "| Validation |      | ✔️      | ✔️         |\n",
    "| Test    |       |  once   | once         |\n",
    "| Deployment    |       |       | ✔️         |\n",
    "\n",
    "You can typically expect $E_{train} < E_{validation} < E_{test} < E_{deployment}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### ❓❓ Questions on generalization and data splitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "```{admonition} Exercise 3.1: True or False\n",
    "1. A decision tree model with no depth is likely to perform very well on the deployment data. \n",
    "2. Data splitting helps us generalize our model better. \n",
    "3. Deployment data is used at the very end and only scored once.  \n",
    "4. Validation data could be used for hyperparameter optimization. \n",
    "```    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "```{admonition} Exercise 3.1: V's Solutions!\n",
    ":class: tip, dropdown\n",
    "1. False\n",
    "2. False. Data splitting helps us **assess** how well our model would generalize. \n",
    "3. False. You cannot score on the deployment data as there are no labels available.\n",
    "4. True\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "```{admonition} Exercise 3.2: Questions for discussion\n",
    "1. Why you can typically expect $E_{train} < E_{validation} < E_{test} < E_{deployment}$. (From Piazza.)\n",
    "2. Discuss the consequences of not shuffling before splitting the data in `train_test_split`. \n",
    "```    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Cross-validation [[video](https://youtu.be/4cv8VYonepA)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Problems with single train/validation split\n",
    "\n",
    "- Only using a portion of your data for training and only a portion for validation.\n",
    "- If your dataset is small you might end up with a tiny training and/or validation set.\n",
    "- You might be unlucky with your splits such that they don't align well or don't well represent your test data.\n",
    "\n",
    "![](img/train-valid-test-split.png)\n",
    "\n",
    "<!-- <img src='img/train-valid-test-split.png' width=\"1500\" height=\"1500\" /> -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Cross-validation to the rescue!! \n",
    "\n",
    "- Cross-validation provides a solution to this problem. \n",
    "- Split the data into $k$ folds ($k>2$, often $k=10$). In the picture below $k=4$.\n",
    "- Each \"fold\" gets a turn at being the validation set.\n",
    "- Note that cross-validation doesn't shuffle the data; it's done in `train_test_split`.\n",
    "\n",
    "![](img/cross-validation.png)\n",
    "<!-- <img src='img/cross-validation.png' width=\"1500\"> -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Each fold gives a score and we usually average our $k$ results. \n",
    "- It's better to examine the variation in the scores across folds.  \n",
    "- Gives a more **robust** measure of error on unseen data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Cross-validation using `scikit-learn`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# Let's demonstrate this with the canada usa cities data\n",
    "# The data is available in the data directory\n",
    "df = pd.read_csv(\"data/canada_usa_cities.csv\")\n",
    "X = df.drop(columns=[\"country\"])\n",
    "y = df[\"country\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
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       "      <td>55.9773</td>\n",
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       "      <td>-134.4197</td>\n",
       "      <td>58.3019</td>\n",
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       "      <td>48.9854</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.7436</td>\n",
       "      <td>48.9881</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.2691</td>\n",
       "      <td>48.9951</td>\n",
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       "      <th>...</th>\n",
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       "      <td>45.3990</td>\n",
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       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>-66.6458</td>\n",
       "      <td>45.9664</td>\n",
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       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>-79.2506</td>\n",
       "      <td>42.9931</td>\n",
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       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>-72.9406</td>\n",
       "      <td>45.6275</td>\n",
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       "      <th>208</th>\n",
       "      <td>-79.4608</td>\n",
       "      <td>46.3092</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>209 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     longitude  latitude\n",
       "0    -130.0437   55.9773\n",
       "1    -134.4197   58.3019\n",
       "2    -123.0780   48.9854\n",
       "3    -122.7436   48.9881\n",
       "4    -122.2691   48.9951\n",
       "..         ...       ...\n",
       "204   -72.7218   45.3990\n",
       "205   -66.6458   45.9664\n",
       "206   -79.2506   42.9931\n",
       "207   -72.9406   45.6275\n",
       "208   -79.4608   46.3092\n",
       "\n",
       "[209 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.discrete_scatter(X.iloc[:, 0], X.iloc[:, 1], y, s=12)\n",
    "plt.xlabel(\"longitude\")\n",
    "plt.ylabel(\"latitude\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df, test_df = train_test_split(\n",
    "    df, test_size=0.2, random_state=123\n",
    ")  # 80%-20% train test split on df\n",
    "X_train, y_train = train_df.drop(columns=[\"country\"]), train_df[\"country\"]\n",
    "X_test, y_test = test_df.drop(columns=[\"country\"]), test_df[\"country\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### `cross_val_score`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score, cross_validate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.76470588, 0.82352941, 0.70588235, 0.94117647, 0.82352941,\n",
       "       0.82352941, 0.70588235, 0.9375    , 0.9375    , 0.9375    ])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = DecisionTreeClassifier(max_depth=4)\n",
    "cv_scores = cross_val_score(model, X_train, y_train, cv=10)\n",
    "cv_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average cross-validation score = 0.84\n",
      "Standard deviation of cross-validation score = 0.09\n"
     ]
    }
   ],
   "source": [
    "print(f\"Average cross-validation score = {np.mean(cv_scores):.2f}\")\n",
    "print(f\"Standard deviation of cross-validation score = {np.std(cv_scores):.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Under the hood\n",
    "\n",
    "- It creates `cv` folds on the data.\n",
    "- In each fold, it fits the model on the training portion and scores on the validation portion. \n",
    "- The output is a list of validation scores in each fold."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### `cross_validate`\n",
    "\n",
    "- Similar to `cross_val_score` but more powerful.\n",
    "- Lets us access training and validation scores.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_score</th>\n",
       "      <th>train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.004587</td>\n",
       "      <td>0.003887</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.913333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.003548</td>\n",
       "      <td>0.001371</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.906667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.001586</td>\n",
       "      <td>0.000927</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.906667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.001325</td>\n",
       "      <td>0.000920</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.001163</td>\n",
       "      <td>0.000746</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.906667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.001202</td>\n",
       "      <td>0.000829</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.913333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.001190</td>\n",
       "      <td>0.000801</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.001279</td>\n",
       "      <td>0.000818</td>\n",
       "      <td>0.937500</td>\n",
       "      <td>0.900662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.001193</td>\n",
       "      <td>0.000801</td>\n",
       "      <td>0.937500</td>\n",
       "      <td>0.900662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.001205</td>\n",
       "      <td>0.000806</td>\n",
       "      <td>0.937500</td>\n",
       "      <td>0.900662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fit_time  score_time  test_score  train_score\n",
       "0  0.004587    0.003887    0.764706     0.913333\n",
       "1  0.003548    0.001371    0.823529     0.906667\n",
       "2  0.001586    0.000927    0.705882     0.906667\n",
       "3  0.001325    0.000920    0.941176     0.900000\n",
       "4  0.001163    0.000746    0.823529     0.906667\n",
       "5  0.001202    0.000829    0.823529     0.913333\n",
       "6  0.001190    0.000801    0.705882     0.920000\n",
       "7  0.001279    0.000818    0.937500     0.900662\n",
       "8  0.001193    0.000801    0.937500     0.900662\n",
       "9  0.001205    0.000806    0.937500     0.900662"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = cross_validate(model, X_train, y_train, cv=10, return_train_score=True)\n",
    "pd.DataFrame(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fit_time</th>\n",
       "      <td>0.001828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>score_time</th>\n",
       "      <td>0.001191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_score</th>\n",
       "      <td>0.840074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_score</th>\n",
       "      <td>0.906865</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    0\n",
       "fit_time     0.001828\n",
       "score_time   0.001191\n",
       "test_score   0.840074\n",
       "train_score  0.906865"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(pd.DataFrame(scores).mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "```{important}\n",
    "Keep in mind that cross-validation does not return a model. It is not a way to build a model that can be applied to new data. The purpose of cross-validation is to **evaluate** how well the model will generalize to unseen data. \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "```{seealso}\n",
    "Note that both `cross_val_score` and `cross_validate` functions do not shuffle the data. Check out [`StratifiedKFold`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedKFold.html#sklearn.model_selection.StratifiedKFold), where proportions of classes is the same in each fold as they are in the whole dataset. By default, `sklearn` uses `StratifiedKFold` when carrying out cross-validation for classification problems. \n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_cross_validation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Our typical supervised learning set up is as follows: \n",
    "\n",
    "- We are given training data with features `X` and target `y`\n",
    "- We split the data into train and test portions: `X_train, y_train, X_test, y_test`\n",
    "- We carry out hyperparameter optimization using cross-validation on the train portion: `X_train` and `y_train`. \n",
    "- We assess our best performing model on the test portion: `X_test` and `y_test`.  \n",
    "- What we care about is the **test error**, which tells us how well our model can be generalized.\n",
    "- If this test error is \"reasonable\" we deploy the model which will be used on new unseen examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_score</th>\n",
       "      <th>train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.095969</td>\n",
       "      <td>0.000660</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000916</td>\n",
       "      <td>0.000569</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.992857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000840</td>\n",
       "      <td>0.000572</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000841</td>\n",
       "      <td>0.000565</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000827</td>\n",
       "      <td>0.000558</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000831</td>\n",
       "      <td>0.000557</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000837</td>\n",
       "      <td>0.000557</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.985816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.000847</td>\n",
       "      <td>0.000567</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.992908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000832</td>\n",
       "      <td>0.000561</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.000816</td>\n",
       "      <td>0.000554</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fit_time  score_time  test_score  train_score\n",
       "0  0.095969    0.000660    0.875000     1.000000\n",
       "1  0.000916    0.000569    0.875000     0.992857\n",
       "2  0.000840    0.000572    0.875000     1.000000\n",
       "3  0.000841    0.000565    0.687500     1.000000\n",
       "4  0.000827    0.000558    0.812500     1.000000\n",
       "5  0.000831    0.000557    0.812500     1.000000\n",
       "6  0.000837    0.000557    0.866667     0.985816\n",
       "7  0.000847    0.000567    0.600000     0.992908\n",
       "8  0.000832    0.000561    0.666667     1.000000\n",
       "9  0.000816    0.000554    0.733333     1.000000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n",
    "model = DecisionTreeClassifier(max_depth=10)\n",
    "scores = cross_validate(model, X_train, y_train, cv=10, return_train_score=True)\n",
    "pd.DataFrame(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def mean_std_cross_val_scores(model, X_train, y_train, **kwargs):\n",
    "    \"\"\"\n",
    "    Returns mean and std of cross validation\n",
    "    \"\"\"\n",
    "    scores = cross_validate(model, X_train, y_train, **kwargs)\n",
    "\n",
    "    mean_scores = pd.DataFrame(scores).mean()\n",
    "    std_scores = pd.DataFrame(scores).std()\n",
    "    out_col = []\n",
    "\n",
    "    for i in range(len(mean_scores)):\n",
    "        out_col.append((f\"%0.3f (+/- %0.3f)\" % (mean_scores[i], std_scores[i])))\n",
    "\n",
    "    return pd.Series(data=out_col, index=mean_scores.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "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>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_score</th>\n",
       "      <th>train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Decision tree</th>\n",
       "      <td>0.002 (+/- 0.001)</td>\n",
       "      <td>0.001 (+/- 0.001)</td>\n",
       "      <td>0.775 (+/- 0.047)</td>\n",
       "      <td>0.992 (+/- 0.014)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        fit_time         score_time         test_score  \\\n",
       "Decision tree  0.002 (+/- 0.001)  0.001 (+/- 0.001)  0.775 (+/- 0.047)   \n",
       "\n",
       "                     train_score  \n",
       "Decision tree  0.992 (+/- 0.014)  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = {}\n",
    "results[\"Decision tree\"] = mean_std_cross_val_scores(\n",
    "    model, X_train, y_train, return_train_score=True\n",
    ")\n",
    "pd.DataFrame(results).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### ❓❓ Questions on cross-validation\n",
    "\n",
    "https://www.menti.com/csapp772zf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "```{admonition} Exercise 3.3: Cross-validation\n",
    "1. $k$-fold cross-validation calls fit $k$. True or False? \n",
    "2. We use cross-validation to improve model performance. True or False? \n",
    "3. Discuss advantages and disadvantages of cross-validation. \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "```{admonition} Exercise 3.3: V's Solutions!\n",
    ":class: tip, dropdown\n",
    "1. True \n",
    "2. False. We can use it to assess model performance.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Break (5 min)\n",
    "\n",
    "![](img/eva-coffee.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Underfitting, overfitting, the fundamental trade-off, the golden rule [[video](https://youtu.be/Ihay8yE5KTI)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Types of errors\n",
    "\n",
    "Imagine that your train and validation errors do not align with each other. How do you diagnose the problem?  \n",
    "\n",
    "We're going to think about 4 types of errors:\n",
    "\n",
    "- $E_\\textrm{train}$ is your training error (or mean train error from cross-validation).\n",
    "- $E_\\textrm{valid}$ is your validation error (or mean validation error from cross-validation).\n",
    "- $E_\\textrm{test}$ is your test error.\n",
    "- $E_\\textrm{best}$ is the best possible error you could get for a given problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Underfitting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train error:   0.188\n",
      "Validation error:   0.212\n"
     ]
    }
   ],
   "source": [
    "model = DecisionTreeClassifier(max_depth=1)  # decision stump\n",
    "scores = cross_validate(model, X_train, y_train, cv=10, return_train_score=True)\n",
    "print(\"Train error:   %0.3f\" % (1 - np.mean(scores[\"train_score\"])))\n",
    "print(\"Validation error:   %0.3f\" % (1 - np.mean(scores[\"test_score\"])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- If your model is too simple, like `DummyClassifier` or `DecisionTreeClassifier` with `max_depth=1`, it's not going to pick up on some random quirks in the data but it won't even capture useful patterns in the training data.\n",
    "- The model won't be very good in general. Both train and validation errors would be high. This is **underfitting**.\n",
    "- The gap between train and validation error is going to be lower.\n",
    "- $E_\\textrm{best} \\lt E_\\textrm{train} \\lesssim E_\\textrm{valid}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Overfitting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train error:   0.000\n",
      "Validation error:   0.220\n"
     ]
    }
   ],
   "source": [
    "model = DecisionTreeClassifier(max_depth=None)\n",
    "scores = cross_validate(model, X_train, y_train, cv=10, return_train_score=True)\n",
    "print(\"Train error:   %0.3f\" % (1 - np.mean(scores[\"train_score\"])))\n",
    "print(\"Validation error:   %0.3f\" % (1 - np.mean(scores[\"test_score\"])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- If your model is very complex, like a `DecisionTreeClassifier(max_depth=None)`, then you will learn unreliable patterns in order to get every single training example correct.\n",
    "- The training error is going to be very low but there will be a big gap between the training error and the validation error. This is **overfitting**.\n",
    "- In overfitting scenario, usually we'll see: \n",
    "$E_\\textrm{train} \\lt E_\\textrm{best}  \\lt E_\\textrm{valid}$\n",
    "- In general, if $E_\\textrm{train}$ is low, we are likely to be in the overfitting scenario. It is fairly common to have at least a bit of this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- So the validation error does not necessarily decrease with the training error. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\n",
    "results_dict = {\n",
    "    \"depth\": [],\n",
    "    \"mean_train_error\": [],\n",
    "    \"mean_cv_error\": [],\n",
    "    \"std_cv_error\": [],\n",
    "    \"std_train_error\": [],\n",
    "}\n",
    "param_grid = {\"max_depth\": np.arange(1, 16)}\n",
    "\n",
    "for depth in param_grid[\"max_depth\"]:\n",
    "    model = DecisionTreeClassifier(max_depth=depth)\n",
    "    scores = cross_validate(model, X_train, y_train, cv=10, return_train_score=True)\n",
    "    results_dict[\"depth\"].append(depth)\n",
    "    results_dict[\"mean_cv_error\"].append(1 - np.mean(scores[\"test_score\"]))\n",
    "    results_dict[\"mean_train_error\"].append(1 - np.mean(scores[\"train_score\"]))\n",
    "    results_dict[\"std_cv_error\"].append(scores[\"test_score\"].std())\n",
    "    results_dict[\"std_train_error\"].append(scores[\"train_score\"].std())\n",
    "\n",
    "results_df = pd.DataFrame(results_dict)\n",
    "results_df = results_df.set_index(\"depth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_df[[\"mean_train_error\", \"mean_cv_error\"]].plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "- Here, for larger depths we observe that the training error is close to 0 but validation error goes up and down. \n",
    "- As we make more complex models we start encoding random quirks in the data, which are not grounded in reality.  \n",
    "- These random quirks do not generalize well to new data. \n",
    "- This problem of failing to be able to generalize to the validation data or test data is called **overfitting**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### The \"fundamental tradeoff\" of supervised learning:\n",
    "\n",
    "\n",
    "**As you increase model complexity, $E_\\textrm{train}$ tends to go down but $E_\\textrm{valid}-E_\\textrm{train}$ tends to go up.**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Bias vs variance tradeoff \n",
    "\n",
    "- The fundamental trade-off is also called the bias/variance tradeoff in supervised machine learning.\n",
    "\n",
    "**Bias**\n",
    ": the tendency to consistently learn the same wrong thing (high bias corresponds to underfitting), or failing to learn something important\n",
    "\n",
    "**Variance** \n",
    ": the tendency to learn random things irrespective of the real signal (high variance corresponds to overfitting)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "```{seealso} \n",
    "Check out [this article by Pedro Domingos](https://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf) for some approachable explanation on machine learning fundamentals and bias-variance tradeoff. \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### How to pick a model that would generalize better?\n",
    "\n",
    "- We want to avoid both underfitting and overfitting. \n",
    "- We want to be consistent with the training data but we don't to rely too much on it. \n",
    "\n",
    "<!-- <center>\n",
    "<img src='img/malp_0201.png' width=\"800\" height=\"800\" />\n",
    "</center>    \n",
    " -->\n",
    "![](img/malp_0201.png)\n",
    "\n",
    "[source](https://learning.oreilly.com/library/view/introduction-to-machine/9781449369880/ch02.html#relation-of-model-complexity-to-dataset-size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- There are many subtleties here and there is no perfect answer but a  common practice is to pick the model with minimum cross-validation error. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def cross_validate_std(*args, **kwargs):\n",
    "    \"\"\"Like cross_validate, except also gives the standard deviation of the score\"\"\"\n",
    "    res = pd.DataFrame(cross_validate(*args, **kwargs))\n",
    "    res_mean = res.mean()\n",
    "    res_mean[\"std_test_score\"] = res[\"test_score\"].std()\n",
    "    if \"train_score\" in res:\n",
    "        res_mean[\"std_train_score\"] = res[\"train_score\"].std()\n",
    "    return res_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_train_error</th>\n",
       "      <th>mean_cv_error</th>\n",
       "      <th>std_cv_error</th>\n",
       "      <th>std_train_error</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>depth</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.171657</td>\n",
       "      <td>0.211250</td>\n",
       "      <td>0.048378</td>\n",
       "      <td>0.006805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.160258</td>\n",
       "      <td>0.223750</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.007316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.142467</td>\n",
       "      <td>0.210833</td>\n",
       "      <td>0.067757</td>\n",
       "      <td>0.022848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.092604</td>\n",
       "      <td>0.204167</td>\n",
       "      <td>0.070907</td>\n",
       "      <td>0.006531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.083338</td>\n",
       "      <td>0.191250</td>\n",
       "      <td>0.067120</td>\n",
       "      <td>0.010650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.066960</td>\n",
       "      <td>0.191250</td>\n",
       "      <td>0.072707</td>\n",
       "      <td>0.012065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.044873</td>\n",
       "      <td>0.210417</td>\n",
       "      <td>0.089136</td>\n",
       "      <td>0.009059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.029909</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.092331</td>\n",
       "      <td>0.009422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.020653</td>\n",
       "      <td>0.209583</td>\n",
       "      <td>0.091819</td>\n",
       "      <td>0.010294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.010679</td>\n",
       "      <td>0.216250</td>\n",
       "      <td>0.088397</td>\n",
       "      <td>0.007938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.005699</td>\n",
       "      <td>0.222083</td>\n",
       "      <td>0.109323</td>\n",
       "      <td>0.004264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.003561</td>\n",
       "      <td>0.235833</td>\n",
       "      <td>0.083836</td>\n",
       "      <td>0.004769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.229583</td>\n",
       "      <td>0.089354</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.242083</td>\n",
       "      <td>0.086932</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.235833</td>\n",
       "      <td>0.092687</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean_train_error  mean_cv_error  std_cv_error  std_train_error\n",
       "depth                                                                \n",
       "1              0.171657       0.211250      0.048378         0.006805\n",
       "2              0.160258       0.223750      0.062723         0.007316\n",
       "3              0.142467       0.210833      0.067757         0.022848\n",
       "4              0.092604       0.204167      0.070907         0.006531\n",
       "5              0.083338       0.191250      0.067120         0.010650\n",
       "6              0.066960       0.191250      0.072707         0.012065\n",
       "7              0.044873       0.210417      0.089136         0.009059\n",
       "8              0.029909       0.210000      0.092331         0.009422\n",
       "9              0.020653       0.209583      0.091819         0.010294\n",
       "10             0.010679       0.216250      0.088397         0.007938\n",
       "11             0.005699       0.222083      0.109323         0.004264\n",
       "12             0.003561       0.235833      0.083836         0.004769\n",
       "13             0.000000       0.229583      0.089354         0.000000\n",
       "14             0.000000       0.242083      0.086932         0.000000\n",
       "15             0.000000       0.235833      0.092687         0.000000"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### test score vs. cross-validation score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The minimum validation error is 0.191 at max_depth = 5 \n"
     ]
    }
   ],
   "source": [
    "best_depth = results_df.index.values[np.argmin(results_df[\"mean_cv_error\"])]\n",
    "print(\n",
    "    \"The minimum validation error is %0.3f at max_depth = %d \"\n",
    "    % (\n",
    "        np.min(results_df[\"mean_cv_error\"]),\n",
    "        best_depth,\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "- Let's pick `max_depth`= 5 and try this model on the test set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error on test set: 0.19\n"
     ]
    }
   ],
   "source": [
    "model = DecisionTreeClassifier(max_depth=best_depth)\n",
    "model.fit(X_train, y_train)\n",
    "print(f\"Error on test set: {1 - model.score(X_test, y_test):.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- The test error is comparable with the cross-validation error. \n",
    "- Do we feel confident that this model would give similar performace when deployed? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### The golden rule <a name=\"4\"></a>\n",
    "\n",
    "- Even though we care the most about test error **THE TEST DATA CANNOT INFLUENCE THE TRAINING PHASE IN ANY WAY**. \n",
    "- We have to be very careful not to violate it while developing our ML pipeline. \n",
    "- Even experts end up breaking it sometimes which leads to misleading results and lack of generalization on the real data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Golden rule violation: Example 1  \n",
    "\n",
    "![](img/golden_rule_violation.png)\n",
    "\n",
    "<!-- <center>\n",
    "<img src='img/golden_rule_violation.png' width=\"500\" height=\"500\" />\n",
    "</center>    \n",
    " -->\n",
    " \n",
    "<blockquote>\n",
    "   ... He attempted to reproduce the research, and found a major flaw: there was some overlap in the data used to both train and test the model. \n",
    "</blockquote>    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Golden rule violation: Example 2  \n",
    "\n",
    "<!-- <center>\n",
    "<img src='img/golden_rule_violation_2.png' width=\"500\" height=\"500\" />\n",
    "</center>    \n",
    " -->\n",
    " \n",
    "![](img/golden_rule_violation_2.png)\n",
    "\n",
    "<blockquote>\n",
    "  ... The Challenge rules state that you must only test your code twice a week, because there’s an element of chance to the results. Baidu has admitted that it used multiple email accounts to test its code roughly 200 times in just under six months – over four times what the rules allow.\n",
    "</blockquote>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### How can we avoid violating golden rule? \n",
    "\n",
    "- Recall that when we split data, we put our test set in an imaginary vault.\n",
    "\n",
    "<!-- <center>\n",
    "<img src='img/train-test-split.png' width=\"1500\" height=\"1500\" />\n",
    "</center>    \n",
    " -->\n",
    " \n",
    "![](img/train-test-split.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Here is the workflow we'll generally follow. \n",
    "\n",
    "- **Splitting**: Before doing anything, split the data `X` and `y` into `X_train`, `X_test`, `y_train`, `y_test` or `train_df` and `test_df` using `train_test_split`. \n",
    "- **Select the best model using cross-validation**: Use `cross_validate` with `return_train_score = True` so that we can get access to training scores in each fold. (If we want to plot train vs validation error plots, for instance.) \n",
    "- **Scoring on test data**: Finally score on the test data with the chosen hyperparameters to examine the generalization performance.\n",
    "\n",
    "**Again, there are many subtleties here we'll discuss the golden rule multiple times throughout the course and in the program.**  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### ❓❓ Questions for you"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "```{admonition} Exercise 3.4\n",
    "\n",
    "Underfitting or overfitting? \n",
    "1. If the mean train accuracy is much higher than the mean cross-validation accuracy.\n",
    "2. If the mean train accuracy and the mean cross-validation accuracy are both low and relatively similar in value.\n",
    "3. Decision tree with no limit on the depth. \n",
    "4. Decision stump on a complicated classification problem. \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "```{admonition} Exercise 3.4: V's Solutions!\n",
    ":class: tip, dropdown\n",
    "1. Overfitting\n",
    "2. Underfitting\n",
    "3. Overfitting\n",
    "4. Underfitting\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "```{admonition} Exercise 3.5\n",
    " \n",
    "State whether True/False. \n",
    "\n",
    "1. In supervised learning, the training error is always lower than the validation error.\n",
    "2. The fundamental tradeoff of ML states that as training error goes down, validation error goes up.\n",
    "3. More \"complicated\" models are more likely to overfit than \"simple\" ones.\n",
    "4. If our training error is extremely low, we are likely to be overfitting.\n",
    "5. A very simple model (e.g. decision stump) has high variance.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "```{admonition} Exercise 3.5: V's Solutions!\n",
    ":class: tip, dropdown\n",
    "1. False\n",
    "2. False\n",
    "3. True\n",
    "4. True\n",
    "5. False\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## What did we learn today? \n",
    "\n",
    "- Importance of generalization in supervised machine learning \n",
    "- Data splitting as a way to approximate generalization error \n",
    "- Train, test, validation, deployment data\n",
    "- Cross-validation\n",
    "- A typical sequence of steps to train supervised machine learning models\n",
    "    - training the model on the train split\n",
    "    - tuning hyperparamters using the validation split\n",
    "    - checking the generalization performance on the test split \n",
    "- Overfitting, underfitting, the fundamental tradeoff, and the golden rule.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](img/eva-seeyou.png)"
   ]
  }
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