lectures/02_decision-trees.ipynb

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    "![](img/330-banner.png)"
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   "cell_type": "markdown",
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    }
   },
   "source": [
    "# Lecture 2: Terminology, Baselines, Decision Trees\n",
    "\n",
    "UBC 2021-22\n",
    "\n",
    "Instructor: Giulia Toti"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
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   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'torchvision'",
     "output_type": "error",
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      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
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      "\u001b[1;32m~\\Desktop\\cpsc330\\cpsc330-2021W2\\lectures\\code/.\\plotting_functions.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mutils\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmglearn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mimageio\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mimread\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mneighbors\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mKNeighborsClassifier\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mKNeighborsRegressor\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\cpsc330\\cpsc330-2021W2\\lectures\\code/.\\utils.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mre\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mPIL\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mImage\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtorchvision\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mvgg16\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtorchvision\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtransforms\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'torchvision'"
     ]
    }
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   "source": [
    "import glob\n",
    "import os\n",
    "import re\n",
    "import sys\n",
    "from collections import Counter, defaultdict\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "sys.path.append(\"code/.\")\n",
    "import graphviz\n",
    "import IPython\n",
    "import mglearn\n",
    "from IPython.display import HTML, display\n",
    "from plotting_functions import *\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline, make_pipeline\n",
    "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor, export_graphviz\n",
    "from utils import *\n",
    "\n",
    "plt.rcParams[\"font.size\"] = 16\n",
    "pd.set_option(\"display.max_colwidth\", 200)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Announcements \n",
    "\n",
    "- Check out this [new paragraph](https://github.com/UBC-CS/cpsc330-2021W2#watch-out-for-) on readme.\n",
    "- Reminder of first deliverables: \n",
    "    - Syllabus quiz: Jan 17, 11:59pm\n",
    "    - Homework 1 (hw1): Jan 17, 11:59pm \n",
    "- You can find the tentative due dates for all deliverables [here](https://github.com/UBC-CS/cpsc330-2021W2#lecture-schedule-tentative). \n",
    "- Please monitor Piazza (especially pinned posts and instructor posts) for announcements. This one on Gradescope is particularly important: https://piazza.com/class/ky0j51i4ud64t5?cid=26\n",
    "- Sorry for the setup difficulties. That said, you are likely to go through this in future and figuring out these things on your own is a valuable skill.\n",
    "- Let's check your status: https://www.menti.com/9edm7kwtnf\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Quick recap: True or False?  (Zoom poll)\n",
    "\n",
    "- There are different types of machine learning. \n",
    "- Predicting spam and predicting housing prices are both examples of supervised machine learning. \n",
    "- For problems such as spelling correction, translation, face recognition, spam identification, if you are a domain expert, it's usually faster and scalable to come up with a robust set of rules manually rather than building a machine learning model. \n",
    "- Google News is likely be using machine learning to organize news. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Learning outcomes \n",
    "From this lecture, you will be able to \n",
    "\n",
    "### Terminology [[video](https://youtu.be/YNT8n4cXu4A)]\n",
    "- identify whether a given problem could be solved using supervised machine learning or not; \n",
    "- differentiate between supervised and unsupervised machine learning;\n",
    "- explain machine learning terminology such as features, targets, predictions, training, and error;\n",
    "- differentiate between classification and regression problems;\n",
    "\n",
    "### Baselines [[video](https://youtu.be/6eT5cLL-2Vc)]\n",
    "- use `DummyClassifier` and `DummyRegressor` as baselines for machine learning problems;\n",
    "- explain the `fit` and `predict` paradigm and use `score` method of ML models; \n",
    "\n",
    "### Decision trees [[video](https://youtu.be/Hcf19Ij35rA)]\n",
    "- broadly describe how decision tree prediction works;\n",
    "- use `DecisionTreeClassifier` and `DecisionTreeRegressor` to build decision trees using `scikit-learn`; \n",
    "- visualize decision trees; \n",
    "\n",
    "### More terminology [[video](https://youtu.be/KEtsfXn4w2E)]\n",
    "- explain the difference between parameters and hyperparameters; \n",
    "- explain the concept of decision boundaries;\n",
    "- explain the relation between model complexity and decision boundaries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Big picture and datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this lecture, we'll talk about our first machine learning model: Decision trees. We will also familiarize ourselves with some common terminology in supervised machine learning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Toy datasets \n",
    "Later in the course we will use larger datasets from Kaggle, for instance. But for our first couple of lectures, we will be working with the following three toy datasets:  \n",
    "\n",
    "- [Quiz2 grade prediction classification dataset](data/quiz2-grade-toy-classification.csv)\n",
    "- [Quiz2 grade prediction regression dataset](data/quiz2-grade-toy-regression.csv)\n",
    "- [Canada USA cities dataset](data/canada_usa_cities.csv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "```{note} \n",
    "If it's not necessary for you to understand the code, I will put it in one of the files under the `code` directory to avoid clutter in this notebook. For example, most of the plotting code is going to be in `code/plotting_functions.py`. \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Terminology [[video](https://youtu.be/YNT8n4cXu4A)]\n",
    "\n",
    "You will see a lot of variable terminology in machine learning and statistics. Let's familiarize ourselves with some of the basic terminology used in ML. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{seealso}\n",
    "Check out [the accompanying video](https://youtu.be/YNT8n4cXu4A) on this material. \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "I'll be using the following grade prediction toy dataset to demonstrate the terminology. Imagine that you are taking a course with four homework assignments and two quizzes. You and your friends are quite nervous about your quiz2 grades and you want to know how will you do based on your previous performance and some other attributes. So you decide to collect some data from your friends from last year and train a supervised machine learning model for quiz2 grade prediction. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(21, 8)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ml_experience</th>\n",
       "      <th>class_attendance</th>\n",
       "      <th>lab1</th>\n",
       "      <th>lab2</th>\n",
       "      <th>lab3</th>\n",
       "      <th>lab4</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>92</td>\n",
       "      <td>93</td>\n",
       "      <td>84</td>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>90</td>\n",
       "      <td>80</td>\n",
       "      <td>83</td>\n",
       "      <td>91</td>\n",
       "      <td>not A+</td>\n",
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       "      <td>85</td>\n",
       "      <td>83</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>not A+</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>91</td>\n",
       "      <td>94</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "      <td>89</td>\n",
       "      <td>A+</td>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>83</td>\n",
       "      <td>90</td>\n",
       "      <td>92</td>\n",
       "      <td>85</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   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+"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classification_df = pd.read_csv(\"data/quiz2-grade-toy-classification.csv\")\n",
    "print(classification_df.shape)\n",
    "classification_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Recap: Supervised machine learning\n",
    "\n",
    "![](img/sup-learning.png)\n",
    "<!-- <img src=\"img/sup-learning.png\" height=\"800\" width=\"800\">  -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Tabular data\n",
    "In supervised machine learning, the input data is typically organized in a **tabular** format, where rows are **examples** and columns are **features**. One of the columns is typically the **target**. \n",
    "\n",
    "![](img/sup-ml-terminology.png)\n",
    "\n",
    "<!-- <img src=\"img/sup-ml-terminology.png\" height=\"1000\" width=\"1000\">  -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "**Features** \n",
    ": Features are relevant characteristics of the problem, usually suggested by experts. Features are typically denoted by $X$ and the number of features is usually denoted by $d$.  \n",
    "\n",
    "**Target**\n",
    ": Target is the feature we want to predict (typically denoted by $y$). \n",
    "\n",
    "**Example** \n",
    ": A row of feature values. When people refer to an example, it may or may not include the target corresponding to the feature values, depending upon the context. The number of examples is usually denoted by $n$. \n",
    "\n",
    "**Training**\n",
    ": The process of learning the mapping between the features ($X$) and the target ($y$). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Example: Tabular data for grade prediction\n",
    "\n",
    "The tabular data usually contains both: the features (`X`) and the target (`y`). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "classification_df = pd.read_csv(\"data/quiz2-grade-toy-classification.csv\")\n",
    "classification_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "So the first step in training a supervised machine learning model is separating `X` and `y`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "X = classification_df.drop(columns=[\"quiz2\"])\n",
    "y = classification_df[\"quiz2\"]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "#### Example: Tabular data for the housing price prediction\n",
    "\n",
    "Here is an example of tabular data for housing price prediction. You can download the data from [here](https://www.kaggle.com/harlfoxem/housesalesprediction). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'data/kc_house_data.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\KIERAN~1\\AppData\\Local\\Temp/ipykernel_17136/946453708.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mhousing_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"data/kc_house_data.csv\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mhousing_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"id\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"date\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mHTML\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhousing_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_html\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\cpsc330\\lib\\site-packages\\pandas\\util\\_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    310\u001b[0m                 )\n\u001b[1;32m--> 311\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    312\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    313\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\cpsc330\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m    584\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    585\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 586\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    587\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    588\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\cpsc330\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    480\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    481\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 482\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    483\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    484\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\cpsc330\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    809\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    810\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 811\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    812\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    813\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\cpsc330\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1038\u001b[0m             )\n\u001b[0;32m   1039\u001b[0m         \u001b[1;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1040\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# type: ignore[call-arg]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1041\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1042\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\cpsc330\\lib\\site-packages\\pandas\\io\\parsers\\c_parser_wrapper.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m     49\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m         \u001b[1;31m# open handles\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 51\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     52\u001b[0m         \u001b[1;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     53\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\cpsc330\\lib\\site-packages\\pandas\\io\\parsers\\base_parser.py\u001b[0m in \u001b[0;36m_open_handles\u001b[1;34m(self, src, kwds)\u001b[0m\n\u001b[0;32m    220\u001b[0m         \u001b[0mLet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mreaders\u001b[0m \u001b[0mopen\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdone\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    221\u001b[0m         \"\"\"\n\u001b[1;32m--> 222\u001b[1;33m         self.handles = get_handle(\n\u001b[0m\u001b[0;32m    223\u001b[0m             \u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    224\u001b[0m             \u001b[1;34m\"r\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\cpsc330\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    700\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;34m\"b\"\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    701\u001b[0m             \u001b[1;31m# Encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 702\u001b[1;33m             handle = open(\n\u001b[0m\u001b[0;32m    703\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    704\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'data/kc_house_data.csv'"
     ]
    }
   ],
   "source": [
    "housing_df = pd.read_csv(\"data/kc_house_data.csv\")\n",
    "housing_df.drop([\"id\", \"date\"], axis=1, inplace=True)\n",
    "HTML(housing_df.head().to_html(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "X = housing_df.drop(columns=[\"price\"])\n",
    "y = housing_df[\"price\"]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "```{admonition} Attention\n",
    ":class: important\n",
    "To a machine, column names (features) have no meaning. Only feature values and how they vary across examples mean something. \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Alternative terminology for examples, features, targets, and training\n",
    "\n",
    "- **examples** = rows = samples = records = instances \n",
    "- **features** = inputs = predictors = explanatory variables = regressors = independent variables = covariates\n",
    "- **targets** = outputs = outcomes = response variable = dependent variable = labels (if categorical).\n",
    "- **training** = learning = fitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{seealso} \n",
    "Check out [the MDS terminology document](https://ubc-mds.github.io/resources_pages/terminology/). \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's pause our grades investigation for a second and think about the content of video [2.1](https://youtu.be/YNT8n4cXu4A). In this video, you learned about the differences between unsupervised and supervised learning, and between classification and regression. The following slides are a quick recap."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Supervised learning vs. Unsupervised learning\n",
    "\n",
    "In **supervised learning**, training data comprises a set of features ($X$) and their corresponding targets ($y$). We wish to find a **model function $f$** that relates $X$ to $y$. Then use that model function **to predict the targets** of new examples. \n",
    "\n",
    "\n",
    "![](img/sup-learning.png)\n",
    "\n",
    "<!-- <img src=\"img/sup-learning.png\" height=\"900\" width=\"900\"> -->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "In **unsupervised learning** training data consists of observations ($X$) **without any corresponding targets**. Unsupervised learning could be used to **group similar things together** in $X$ or to provide **concise summary** of the data. We'll learn more about this topic in later videos.\n",
    "\n",
    "![](img/unsup-learning.png)\n",
    "\n",
    "<!-- <img src=\"img/unsup-learning.png\" alt=\"\" height=\"900\" width=\"900\"> -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Supervised machine learning is about function approximation, i.e., finding the mapping function between `X` and `y` whereas unsupervised machine learning is about concisely describing the data.   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Classification vs. Regression \n",
    "In supervised machine learning, there are two main kinds of learning problems based on what they are trying to predict.\n",
    "- **Classification problem**: predicting among two or more discrete classes\n",
    "    - Example1: Predict whether a patient has a liver disease or not\n",
    "    - Example2: Predict whether a student would get an A+ or not in quiz2.  \n",
    "- **Regression problem**: predicting a continuous value\n",
    "    - Example1: Predict housing prices \n",
    "    - Example2: Predict a student's score in quiz2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![](img/classification-vs-regression.png)\n",
    "<!-- <img src=\"img/classification-vs-regression.png\" height=\"1500\" width=\"1500\">  -->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# quiz2 classification toy data\n",
    "classification_df = pd.read_csv(\"data/quiz2-grade-toy-classification.csv\")\n",
    "classification_df.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "# quiz2 regression toy data\n",
    "regression_df = pd.read_csv(\"data/quiz2-grade-toy-regression.csv\")\n",
    "regression_df.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### ❓❓ Questions for you"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "```{admonition} Exercise 2.2: Supervised vs. unsupervised \n",
    "\n",
    "Which of these are examples of supervised learning?\n",
    "\n",
    "1. Finding groups of similar properties in a real estate data set.\n",
    "2. Predicting real estate prices based on house features like number of rooms, learning from past sales as examples.\n",
    "3. Grouping articles on different topics from different news sources (something like the Google News app). \n",
    "4. Detecting credit card fraud based on examples of fraudulent and non-fraudulent transactions.\n",
    "```\n",
    "\n",
    "Let's answer here: https://www.menti.com/xhiy7p22yp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "```{admonition} Exercise 2.2: V's Solutions!\n",
    ":class: tip, dropdown\n",
    "- 2 and 4 are examples of supervised machine learning \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "```{admonition} Exercise 2.3: Classification vs. Regression\n",
    "\n",
    "Which of these are examples of classification and which ones are of regression?\n",
    "\n",
    "1. Predicting the price of a house based on features such as number of bedrooms and the year built.\n",
    "2. Predicting if a house will sell or not based on features like the price of the house, number of rooms, etc.\n",
    "3. Predicting percentage grade in CPSC 330 based on past grades.\n",
    "4. Predicting whether you should bicycle tomorrow or not based on the weather forecast.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "```{admonition} Exercise 2.3: V's Solutions!\n",
    ":class: tip, dropdown\n",
    "- classification: 2, 4 \n",
    "- regression: 1, 3\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Baselines [[video](https://youtu.be/6eT5cLL-2Vc)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{seealso}\n",
    "Check out [the accompanying video](https://youtu.be/6eT5cLL-2Vc) on this material. \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### Supervised learning (Reminder)\n",
    "\n",
    "- Training data $\\rightarrow$ Machine learning algorithm $\\rightarrow$ ML model \n",
    "- Unseen test data + ML model $\\rightarrow$ predictions\n",
    "![](img/sup-learning.png)\n",
    "<!-- <img src=\"img/sup-learning.png\" height=\"1000\" width=\"1000\">  -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "\n",
    "Let's build a very simple supervised machine learning model for quiz2 grade prediction problem. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "classification_df = pd.read_csv(\"data/quiz2-grade-toy-classification.csv\")\n",
    "classification_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "classification_df['quiz2'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seems like \"not A+\" occurs more frequently than \"A+\". What if we predict \"not A+\" all the time? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Baselines \n",
    "\n",
    "**Baseline**\n",
    ": A simple machine learning algorithm based on simple rules of thumb. \n",
    "\n",
    "- For example, most frequent baseline always predicts the most frequent label in the training set. \n",
    "- Baselines provide a way to sanity check your machine learning model.    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### `DummyClassifier` \n",
    "\n",
    "- `sklearn`'s baseline model for classification  \n",
    "- Let's train `DummyClassifier` on the grade prediction dataset. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Steps to train a classifier using `sklearn` \n",
    "\n",
    "1. Read the data\n",
    "2. Create $X$ and $y$\n",
    "3. Create a classifier object\n",
    "4. `fit` the classifier\n",
    "5. `predict` on new examples\n",
    "6. `score` the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Reading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "classification_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Create $X$ and $y$\n",
    "\n",
    "- $X$ &rarr; Feature vectors\n",
    "- $y$ &rarr; Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "X = classification_df.drop(columns=[\"quiz2\"])\n",
    "y = classification_df[\"quiz2\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Create a classifier object\n",
    "\n",
    "- `import` the appropriate classifier \n",
    "- Create an object of the classifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.dummy import DummyClassifier # import the classifier\n",
    "\n",
    "dummy_clf = DummyClassifier(strategy=\"most_frequent\") # Create a classifier object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### `fit` the classifier\n",
    "\n",
    "- The \"learning\" is carried out when we call `fit` on the classifier object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "dummy_clf.fit(X, y); # fit the classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### `predict` the target of given examples\n",
    "\n",
    "- We can predict the target of examples by calling `predict` on the classifier object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "dummy_clf.predict(X) # predict using the trained classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### `score` your model\n",
    "\n",
    "- How do you know how well your model is doing?\n",
    "- For classification problems, by default, `score` gives the **accuracy** of the model, i.e., proportion of correctly predicted targets.  \n",
    "\n",
    "    $accuracy = \\frac{\\text{correct predictions}}{\\text{total examples}}$   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"The accuracy of the model on the training data: %0.3f\" % (dummy_clf.score(X, y)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Sometimes you will also see people reporting **error**, which is usually $1 - accuracy$ \n",
    "- `score` \n",
    "    - calls `predict` on `X` \n",
    "    - compares predictions with `y` (true targets)\n",
    "    - returns the accuracy in case of classification.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\n",
    "    \"The error of the model on the training data: %0.3f\" % (1 - dummy_clf.score(X, y))\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### `fit`, `predict` , and `score` summary\n",
    "\n",
    "Here is the general pattern when we build ML models using `sklearn`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "# Create `X` and `y` from the given data\n",
    "X = classification_df.drop(columns=[\"quiz2\"])\n",
    "y = classification_df[\"quiz2\"]\n",
    "\n",
    "clf = DummyClassifier(strategy=\"most_frequent\") # Create a class object\n",
    "clf.fit(X, y) # Train/fit the model\n",
    "print(clf.score(X, y)) # Assess the model\n",
    "\n",
    "new_examples = [[0, 1, 92, 90, 95, 93, 92], [1, 1, 92, 93, 94, 92]]\n",
    "clf.predict(new_examples) # Predict on some new data using the trained model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### [`DummyRegressor`](https://scikit-learn.org/0.15/modules/generated/sklearn.dummy.DummyRegressor.html)\n",
    "\n",
    "You can also do the same thing for regression problems using `DummyRegressor`.\n",
    "\n",
    "If the DummyClassifier picked the most \"popular\" target, what should the DummyRegressor Pick?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Let's build a regression baseline model using `sklearn`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.dummy import DummyRegressor\n",
    "\n",
    "regression_df = pd.read_csv(\"data/quiz2-grade-toy-regression.csv\") # Read data \n",
    "X = regression_df.drop(columns=[\"quiz2\"]) # Create `X` and `y` from the given data\n",
    "y = regression_df[\"quiz2\"]\n",
    "reg = DummyRegressor() # Create a class object; uses mean as default strategy\n",
    "reg.fit(X, y) # Train/fit the model\n",
    "reg.score(X, y) # Assess the model\n",
    "new_examples = [[0, 1, 92, 90, 95, 93, 92], [1, 1, 92, 93, 94, 92]]\n",
    "reg.predict(new_examples) # Predict on some new data using the trained model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- The `fit` and `predict` paradigms similar to classification. The `score` method in the context of regression returns somethings called [$R^2$ score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html#sklearn.metrics.r2_score). (More on this in later videos.)     \n",
    "    - The maximum $R^2$ is 1 for perfect predictions. \n",
    "    - For `DummyRegressor` it returns the mean of the `y` values.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg.score(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### ❓❓ Questions for you"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{admonition} Exercise 2.4\n",
    "1. Order the steps below to build ML models using `sklearn`. \n",
    "    - `score` to evaluate the performance of a given model\n",
    "    - `predict` on new examples \n",
    "    - Creating a model instance\n",
    "    - Creating `X` and `y` \n",
    "    - `fit`\n",
    "2. Why do we train a Dummy Classifier?\n",
    "\n",
    "```\n",
    "\n",
    "Let's answer here: https://www.menti.com/n5poogb78r"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Decision trees [[video](https://youtu.be/Hcf19Ij35rA)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{seealso}\n",
    "Check out [the accompanying video](https://youtu.be/Hcf19Ij35rA) on this material. \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Writing a traditional program to predict quiz2 grade\n",
    "\n",
    "- Can we do better than the baseline? \n",
    "- Forget about ML for a second. If you are asked to write a program to predict whether a student gets an A+ or not in quiz2, how would you go for it?  \n",
    "- For simplicity, let's binarize the feature values. \n",
    "\n",
    "![](img/quiz2-grade-toy.png)\n",
    "\n",
    "<!-- <img src=\"img/quiz2-grade-toy.png\" height=\"700\" width=\"700\">  -->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Is there a pattern that distinguishes yes's from no's and what does the pattern say about today? \n",
    "- How about a rule-based algorithm with a number of *if else* statements?  \n",
    "    ```\n",
    "    if class_attendance == 1 and quiz1 == 1:\n",
    "        quiz2 == \"A+\"\n",
    "    elif class_attendance == 1 and lab3 == 1 and lab4 == 1:\n",
    "        quiz2 == \"A+\"\n",
    "    ...\n",
    "    ```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- How many possible rule combinations there could be with the given 7 binary features? \n",
    "    - Gets unwieldy pretty quickly "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Decision tree algorithm \n",
    "\n",
    "- A machine learning algorithm to derive such rules from data in a principled way.  \n",
    "- Have you ever played [20-questions game](https://en.wikipedia.org/wiki/Twenty_questions)? Decision trees are based on the same idea! \n",
    "- Let's `fit` a decision tree using `scikit-learn` and `predict` with it.\n",
    "- Recall that `scikit-learn` uses the term `fit` for training or learning and uses `predict` for prediction. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Building decision trees with `sklearn`\n",
    "\n",
    "Let's binarize our toy dataset for simplicity. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classification_df = pd.read_csv(\"data/quiz2-grade-toy-classification.csv\")\n",
    "X = classification_df.drop(columns=[\"quiz2\"])\n",
    "y = classification_df[\"quiz2\"]\n",
    "\n",
    "X_binary = X.copy()\n",
    "columns = [\"lab1\", \"lab2\", \"lab3\", \"lab4\", \"quiz1\"]\n",
    "for col in columns:\n",
    "    X_binary[col] = X_binary[col].apply(lambda x: 1 if x >= 90 else 0)\n",
    "X_binary.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        A+\n",
       "1    not A+\n",
       "2    not A+\n",
       "3        A+\n",
       "4        A+\n",
       "Name: quiz2, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### `DummyClassifier` on quiz2 grade prediction toy dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5238095238095238"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_clf = DummyClassifier(strategy=\"most_frequent\")\n",
    "dummy_clf.fit(X_binary, y)\n",
    "dummy_clf.score(X_binary, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### `DecisionTreeClassifier` on quiz2 grade prediction toy dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9047619047619048"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "model = DecisionTreeClassifier() # Create a decision tree\n",
    "model.fit(X_binary, y) # Fit a decision tree\n",
    "model.score(X_binary, y) # Assess the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The decision tree classifier is giving much higher accuracy than the dummy classifier. That's good news! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
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    "### Some terminology related to trees \n",
    "\n",
    "Here is a commonly used terminology in a typical representation of decision trees. \n",
    "\n",
    "**A root node**\n",
    ": represents the first condition to check or question to ask\n",
    "\n",
    "**A branch**\n",
    ": connects a node (condition) to the next node (condition) in the tree. Each branch typically represents either true or false. \n",
    "\n",
    "**An internal node** \n",
    ": represents conditions within the tree\n",
    "\n",
    "**A leaf node**\n",
    ": represents the predicted class/value when the path from root to the leaf node is followed. \n",
    "\n",
    "**Tree depth**\n",
    ": The number of edges on the path from the root node to the farthest away leaf node."
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    "### How does `predict` work? "
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       "   ml_experience  class_attendance  lab1  lab2  lab3  lab4  quiz1\n",
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    "new_example = np.array([[0, 1, 0, 0, 1, 1, 1]])\n",
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gtoti/opt/anaconda3/envs/cpsc330/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['A+'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(new_example)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "In summary, given a learned tree and a test example, during prediction time,  \n",
    "- Start at the top of the tree. Ask binary questions at each node and follow the appropriate path in the tree. Once you are at a leaf node, you have the prediction. \n",
    "- Note that the model only considers the features which are in the learned tree and ignores all other features. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### How does `fit` work? \n",
    "\n",
    "- Decision tree is inspired by [20-questions game](https://en.wikipedia.org/wiki/Twenty_questions). \n",
    "- Each node either represents a question or an answer. The terminal nodes (called leaf nodes) represent answers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_fruit_tree()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### How does `fit` work? \n",
    "\n",
    "- Which features are most useful for classification? \n",
    "- Minimize **impurity** at each question\n",
    "- Common criteria to minimize impurity: [gini index](https://scikit-learn.org/stable/modules/tree.html#classification-criteria), information gain, cross entropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "model = DecisionTreeClassifier() # Create a decision tree\n",
    "model.fit(X_binary, y) # Fit a decision tree\n",
    "display_tree(X_binary.columns, model)"
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    "```{admonition} Warning \n",
    ":class: warning \n",
    "We won't go through **how** it does this - that's CPSC 340. But it's worth noting that it support two types of inputs: \n",
    "    1. Categorical (e.g., Yes/No or more options, as shown in the tree above)\n",
    "    2. Numeric (a number)In the numeric case, the decision tree algorithm also picks the _threshold_. \n",
    "```"
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   "source": [
    "### Decision trees with continuous features"
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   "execution_count": 13,
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       "   ml_experience  class_attendance  lab1  lab2  lab3  lab4  quiz1\n",
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     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "model = DecisionTreeClassifier()\n",
    "model.fit(X, y)\n",
    "display_tree(X.columns, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Decision tree for regression problems\n",
    "\n",
    "- We can also use decision tree algorithm for regression. \n",
    "- Instead of gini, we use [some other criteria](https://scikit-learn.org/stable/modules/tree.html#mathematical-formulation) for splitting. A common one is mean squared error (MSE). (More on this in later videos.)\n",
    "- `scikit-learn` supports regression using decision trees with `DecisionTreeRegressor` \n",
    "    - `fit` and `predict` paradigms similar to classification\n",
    "    - `score` returns somethings called [$R^2$ score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html#sklearn.metrics.r2_score).     \n",
    "        - The maximum $R^2$ is 1 for perfect predictions. \n",
    "        - It can be negative which is very bad (worse than `DummyRegressor`). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   },
   "outputs": [
    {
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       "      <th>ml_experience</th>\n",
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      "text/plain": [
       "   ml_experience  class_attendance  lab1  lab2  lab3  lab4  quiz1  quiz2\n",
       "0              1                 1    92    93    84    91     92     90\n",
       "1              1                 0    94    90    80    83     91     84\n",
       "2              0                 0    78    85    83    80     80     82\n",
       "3              0                 1    91    94    92    91     89     92\n",
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     "execution_count": 15,
     "metadata": {},
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   "source": [
    "regression_df = pd.read_csv(\"data/quiz2-grade-toy-regression.csv\")\n",
    "regression_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 score on the training data: 0.989\n",
      "\n",
      "\n"
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      "text/plain": [
       "   ml_experience  class_attendance  lab1  lab2  lab3  lab4  quiz1  quiz2  \\\n",
       "0              1                 1    92    93    84    91     92     90   \n",
       "1              1                 0    94    90    80    83     91     84   \n",
       "2              0                 0    78    85    83    80     80     82   \n",
       "3              0                 1    91    94    92    91     89     92   \n",
       "4              0                 1    77    83    90    92     85     90   \n",
       "\n",
       "   predicted_quiz2  \n",
       "0        90.333333  \n",
       "1        83.000000  \n",
       "2        83.000000  \n",
       "3        92.000000  \n",
       "4        90.333333  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = regression_df.drop([\"quiz2\"], axis=1)\n",
    "y = regression_df[\"quiz2\"]\n",
    "\n",
    "depth = 2\n",
    "reg_model = DecisionTreeRegressor(max_depth=depth)\n",
    "reg_model.fit(X, y); \n",
    "regression_df[\"predicted_quiz2\"] = reg_model.predict(X)\n",
    "print(\"R^2 score on the training data: %0.3f\\n\\n\" % (reg_model.score(X, y)))\n",
    "regression_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### ❓❓ Questions for you to ponder on"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "```{admonition} Exercise 2.6 True or False \n",
    "1. For the decision tree algorithm to work, the feature values must be numeric.  \n",
    "2. For the decision tree algorithm to work, the target values must be numeric.\n",
    "3. When working with numeric features, the tree will pick the mean as threshold value.\n",
    "``` "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{admonition} Exercise 2.6: V's Solutions!\n",
    ":class: tip, dropdown\n",
    "1. False\n",
    "2. False\n",
    "3. False\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Break (5 min)\n",
    "\n",
    "![](img/eva-coffee.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## More terminology [[video](https://youtu.be/KEtsfXn4w2E)]\n",
    "\n",
    "- Parameters and hyperparameters\n",
    "- Decision boundary \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{seealso}\n",
    "Check out [the accompanying video](https://youtu.be/KEtsfXn4w2E) on this material. \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Parameters \n",
    "\n",
    "- The decision tree algorithm primarily learns two things: \n",
    "    - the best feature to split on\n",
    "    - the threshold for the feature to split on at each node\n",
    "- These are called **parameters** of the decision tree model.  \n",
    "- When predicting on new examples, we need parameters of the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "classification_df = pd.read_csv(\"data/quiz2-grade-toy-classification.csv\")\n",
    "X = classification_df.drop(columns=[\"quiz2\"])\n",
    "y = classification_df[\"quiz2\"]\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(X, y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
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    "display_tree(X.columns, model, counts=True)"
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    "- With the default setting, the nodes are expanded until all leaves are \"pure\" (or further splits do not improve the classification). "
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    "- The decision tree is creating very specific rules, based on just one example from the data. \n",
    "- Is it possible to control the learning in any way? \n",
    "    - Yes! One way to do it is by controlling the **depth** of the tree, which is the length of the longest path from the tree root to a leaf.    "
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    "### Decision tree with `max_depth=1`\n",
    "\n",
    "**Decision stump**\n",
    ": A decision tree with only one split (depth=1) is called a **decision stump**. "
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    "model = DecisionTreeClassifier(max_depth=1)\n",
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    "`max_depth` is a **hyperparameter** of `DecisionTreeClassifier`. "
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    "### Decision tree with `max_depth=3`"
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    "model = DecisionTreeClassifier(\n",
    "    max_depth=3\n",
    ")  # Let's try another value for the hyperparameter\n",
    "model.fit(X, y)\n",
    "display_tree(X.columns, model, counts=True)"
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    "### Parameters and hyperparameters: Summary \n",
    "**Parameters**\n",
    ": When you call `fit`, a bunch of values get set, like the features to split on and split thresholds. These are called **parameters**. These are learned by the algorithm from the data during training. We need them during prediction time. \n",
    "\n",
    "**Hyperparameters**\n",
    ": Even before calling `fit` on a specific data set, we can set some \"knobs\" that control the learning. These are called **hyperparameters**. These are specified based on: expert knowledge, heuristics, or systematic/automated optimization (more on this in the coming lectures).    "
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    "```{admonition} Attention\n",
    ":class: important\n",
    "In `sklearn` hyperparameters are set in the constructor. \n",
    "```"
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    "Above we looked at the `max_depth` hyperparameter. Some other commonly used hyperparameters of decision tree are:\n",
    "\n",
    "- `min_samples_split`\n",
    "- `min_samples_leaf`\n",
    "- `max_leaf_nodes`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "```{seealso}\n",
    "See [here](https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html) for other hyperparameters of a tree.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Decision boundary \n",
    "\n",
    "What do we do with learned models? So far we have been using them to predict the class of a new instance. Another way to think about them is to ask: what sort of test examples will the model classify as positive, and what sort will it classify as negative? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Example 1: quiz 2 grade prediction \n",
    "\n",
    "For visualization purposes, let's consider a subset of the data with only two features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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>lab4</th>\n",
       "      <th>quiz1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>83</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>91</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>92</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   lab4  quiz1\n",
       "0    91     92\n",
       "1    83     91\n",
       "2    80     80\n",
       "3    91     89\n",
       "4    92     85"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_subset = X[[\"lab4\", \"quiz1\"]]\n",
    "X_subset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "##### Decision boundary for `max_depth=1` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gtoti/opt/anaconda3/envs/cpsc330/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "depth = 1  # decision stump\n",
    "model = DecisionTreeClassifier(max_depth=depth)\n",
    "model.fit(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": "slide"
    }
   },
   "source": [
    "We assume geometric view of the data. Here, the red region corresponds to \"not A+\" class and blue region corresponds to \"A+\" class. And there is a line separating the red region and the blue region which is called the **decision boundary** of the model. Different models have different kinds of decision boundaries. \n",
    "In decision tree models, when we are working with only two features, the decision boundary is made up of horizontal and vertical lines. In the example above, the decision boundary is created by asking one question `lab4 <= 84.5`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "##### Decision boundary for `max_depth=2` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gtoti/opt/anaconda3/envs/cpsc330/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = DecisionTreeClassifier(max_depth=2)\n",
    "model.fit(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": {},
   "source": [
    "The decision boundary, i.e., the model gets a bit more complicated. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "##### Decision boundary for `max_depth=5` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gtoti/opt/anaconda3/envs/cpsc330/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = DecisionTreeClassifier(max_depth=5)\n",
    "model.fit(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": {},
   "source": [
    "The decision boundary, i.e., the model gets even more complicated with `max_depth=5`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Example 2: Predicting country using the longitude and latitude \n",
    "\n",
    "Imagine that you are given longitude and latitude of some border cities of USA and Canada along with which country they belong to. Using this training data, you are supposed to come up with a classification model to predict whether a given longitude and latitude combination is in the USA or Canada. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-130.0437</td>\n",
       "      <td>55.9773</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-134.4197</td>\n",
       "      <td>58.3019</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-123.0780</td>\n",
       "      <td>48.9854</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.7436</td>\n",
       "      <td>48.9881</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.2691</td>\n",
       "      <td>48.9951</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\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",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>-66.6458</td>\n",
       "      <td>45.9664</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>-79.2506</td>\n",
       "      <td>42.9931</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>-72.9406</td>\n",
       "      <td>45.6275</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>-79.4608</td>\n",
       "      <td>46.3092</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>209 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     longitude  latitude country\n",
       "0    -130.0437   55.9773     USA\n",
       "1    -134.4197   58.3019     USA\n",
       "2    -123.0780   48.9854     USA\n",
       "3    -122.7436   48.9881     USA\n",
       "4    -122.2691   48.9951     USA\n",
       "..         ...       ...     ...\n",
       "204   -72.7218   45.3990  Canada\n",
       "205   -66.6458   45.9664  Canada\n",
       "206   -79.2506   42.9931  Canada\n",
       "207   -72.9406   45.6275  Canada\n",
       "208   -79.4608   46.3092  Canada\n",
       "\n",
       "[209 rows x 3 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### US Canada cities data\n",
    "df = pd.read_csv(\"data/canada_usa_cities.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[[\"longitude\", \"latitude\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df[\"country\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": [
    "mglearn.discrete_scatter(X.iloc[:, 0], X.iloc[:, 1], y)\n",
    "plt.xlabel(\"longitude\")\n",
    "plt.ylabel(\"latitude\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "##### Real boundary between Canada and USA\n",
    "\n",
    "In real life we know what's the boundary between USA and Canada. \n",
    "\n",
    "![](img/canada-us-border.jpg)\n",
    "\n",
    "<!-- <img src=\"img/canada-us-border.jpg\" height=\"500\" width=\"500\">  -->\n",
    "\n",
    "[Source](https://sovereignlimits.com/blog/u-s-canada-border-history-disputes)\n",
    "\n",
    "Here we want to pretend that we do not know this boundary and we want to infer this boundary based on the limited training examples given to us. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gtoti/opt/anaconda3/envs/cpsc330/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = DecisionTreeClassifier(max_depth=1)\n",
    "model.fit(X, y)\n",
    "plot_tree_decision_boundary_and_tree(\n",
    "    model,\n",
    "    X,\n",
    "    y,\n",
    "    height=6,\n",
    "    width=16,\n",
    "    eps=10,\n",
    "    x_label=\"longitude\",\n",
    "    y_label=\"latitude\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gtoti/opt/anaconda3/envs/cpsc330/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = DecisionTreeClassifier(max_depth=2)\n",
    "model.fit(X, y)\n",
    "plot_tree_decision_boundary_and_tree(\n",
    "    model,\n",
    "    X,\n",
    "    y,\n",
    "    height=6,\n",
    "    width=16,\n",
    "    eps=10,\n",
    "    x_label=\"longitude\",\n",
    "    y_label=\"latitude\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Practice exercises \n",
    "\n",
    "- If you want more practice, check out module 2 in [this online course](https://ml-learn.mds.ubc.ca/en/module2). All the sections **without** video or notes symbol are exercises.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{admonition} Attention\n",
    ":class: important\n",
    "If all of you are working on the exercises, especially coding exercises, at the same time, you might have to wait for the real-time feedback for a long time or you might even get an error. There is no solution for this other than waiting for a while and trying it again. \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{note}\n",
    "Some background on [the online course](https://ml-learn.mds.ubc.ca/en/) above: This course is designed by Hayley Boyce, Mike Gelbart, and myself. It'll be a great resource at the beginning of this class, as it give you a chance to practice what we learn and the framework will provide you real-time feedback. \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
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    "## Final comments and summary\n",
    "\n",
    "What did we learn today? \n",
    "\n",
    "- There is a lot of terminology and jargon used in ML. Some of the basic \n",
    "terminology includes:\n",
    "    - Features, target, examples, training\n",
    "    - Supervised vs. Unsupervised machine learning     \n",
    "    - Classification and regression    \n",
    "    - Accuracy and error    \n",
    "    - Parameters and hyperparameters\n",
    "    - Decision boundary "
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    "- Baselines and steps to train a supervised machine learning model \n",
    "    - Baselines serve as reference points in ML workflow. "
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    "- Decision trees    \n",
    "    - are models that make predictions by sequentially looking at features and checking whether they are above/below a threshold\n",
    "    - learn a hierarchy of if/else questions, similar to questions you might ask in a 20-questions game.       \n",
    "    - learn axis-aligned decision boundaries (vertical and horizontal lines with 2 features)    \n",
    "    - One way to control the complexity of decision tree models is by using the depth hyperparameter (`max_depth` in `sklearn`). "
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   "source": [
    "![](img/eva-logging-off.png)\n"
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