{ "cells": [ { "cell_type": "markdown", "id": "3feddd85-239d-443c-8e37-7d15e5c45483", "metadata": {}, "source": [ "# **Homework # 2: Linear and Logistic Regression**\n", "\n", "## Overview\n", "This notebook is designed to guide you through the fundamental concepts of Linear and Logistic Regression. By the end of this homework, you will have a solid understanding of how to implement these algorithms from scratch and how to visualize the results.\n", "\n", "### **Learning Objectives:**\n", "\n", "- **Linear Regression**\n", "\n", " Understand the theoretical background\n", " Implement the cost function\n", " Visualize the linear fit\n", " Analyze the relationship between parameters and cost\n", "

 

\n", "\n", "- **Logistic Regression**\n", "\n", " Grasp the theoretical foundations\n", " Implement the sigmoid function\n", " Develop the cost function\n", " Optimize parameters\n", " Visualize the decision boundary\n", "\n", "### **Structure**\n", "\n", "- [**Part 1: Linear Regression (40% of total)**](#1)\n", " - [1.1. Setting up the Environment](#1.1)\n", " - [1.2. Dataset Loading and Visualization](#1.2)\n", " - [1.3. Implementation of the Cost Function](#1.3)\n", " - [1.4. Manual Adjustment of Parameters](#1.4)\n", " - [1.5. Estimating Optimal Parameters](#1.5)\n", " - [1.6. Visualizing the Linear Fit](#1.6)\n", "

 

\n", "\n", "- [**Part 2: Logistic Regression (40% of total)**](#2)\n", " - [2.1. Setting up the Environment](#2.1)\n", " - [2.2. Dataset Loading and Visualization](#2.2)\n", " - [2.3. Splitting the Data and Explanation of Features](#2.3)\n", " - [2.4. Implementation of the Sigmoid Function](#2.4)\n", " - [2.5. Implementation of the Cost Function](#2.5)\n", " - [2.6. Complete the Cost Function Code](#2.6)\n", " - [2.7. Optimizing Parameters](#2.7)\n", " - [2.8. Visualizing the Decision Boundary](#2.8)\n", " - [2.9. Evaluating the Model with a Confusion Matrix](#2.9)\n", " - [2.10. Generating a Classification Report](#2.10)\n", "

 

\n", "\n", "- [**Part 3: Knowledge Check (20% of total)**](#3)\n", "\n", "\n", "\n", "### **Instructions**\n", "\n", "- Read through each section carefully\n", "- Complete the code in areas marked `###YOUR CODE STARTS HERE` and `###YOUR CODE ENDS HERE`\n", "- Run all code cells to see plots and results\n", "- Ask for help if needed, but avoid code sharing\n", "- Utilize online resources responsibly\n", "- Ensure you have necessary libraries installed (numpy, pandas, matplotlib)\n", "- Pay attention to the theory sections to understand the concepts behind the implementations\n", "- For more information click on the `Task Hints` and `Expected Hints` above and below each task.\n", "\n", "### **Getting Help**\n", "\n", "- If you encounter issues or need assistance, here is the preferred approach to find help:\n", "\n", " 1. **Search Online**: Begin by using online resources and forums to find answers to your questions. Make sure to solve problems on your own as much as possible without directly copying code.\n", " \n", " 2. **Consult Peers**: If online resources do not resolve your issue, you may reach out to fellow students for help. Please remember that sharing actual code is prohibited.\n", " \n", " 3. **Class Discord**: If peer consultation doesn't suffice, post your query in the class Discord to seek further assistance.\n", " \n", " 4. **Contact the TA**: If your issue still persists after exploring the above steps, reach out to the Teaching Assistant (TA).\n", " \n", " 5. **Instructor Assistance**: If you still require help and the TA has not been able to resolve your issue, you should contact the instructor.\n", "\n", "\n", "## Tutorials and Documentation Links For Help\n", "\n", "**Here are the links to the tutorials:**\n", "\n", "- [NumPy Tutorial](https://github.com/CuriousNeuralNerd/Tutorials/blob/fbdeb171ed16958e7571df0f1272c68ce3f4d3ab/numpy_basics.ipynb)\n", "- [pandas Tutorial](https://github.com/CuriousNeuralNerd/Tutorials/blob/fbdeb171ed16958e7571df0f1272c68ce3f4d3ab/pandas_basics.ipynb)\n", "- [Matplotlib Tutorial](https://github.com/CuriousNeuralNerd/Tutorials/blob/fbdeb171ed16958e7571df0f1272c68ce3f4d3ab/matplotlib_basics.ipynb)\n", "- [scikit-learn Tutorial](https://github.com/CuriousNeuralNerd/Tutorials/blob/fbdeb171ed16958e7571df0f1272c68ce3f4d3ab/scikit_learn_basics.ipynb)\n", "- [Linear Regression Tutorial](https://github.com/CuriousNeuralNerd/Tutorials/blob/fbdeb171ed16958e7571df0f1272c68ce3f4d3ab/Linear_Regression_Tutorial.ipynb)\n", "- [Logistic Regression Tutorial](https://github.com/CuriousNeuralNerd/Tutorials/blob/fbdeb171ed16958e7571df0f1272c68ce3f4d3ab/Logistic_Regression_Tutorial.ipynb)\n", "\n", "\n", "**Here are the links to the official documentation for the libraries:**\n", "\n", "- [NumPy Documentation](https://numpy.org/doc/stable/user/absolute_beginners.html)\n", "- [pandas Documentation](https://pandas.pydata.org/docs/user_guide/index.html#user-guide)\n", "- [Matplotlib Documentation](https://matplotlib.org/stable/users/explain/quick_start.html)\n", "- [SciPy Documentation](https://scipy.github.io/devdocs/tutorial/index.html)\n", "- [scikit-learn Documentation](https://scikit-learn.org/stable/)\n", "- [Seaborn Documentation](https://seaborn.pydata.org/tutorial/introduction.html)" ] }, { "cell_type": "markdown", "id": "c60fd2b2-2c82-415c-af31-ada3ac3ec869", "metadata": {}, "source": [ "### **Grading Rubric**\n", " \n", "| **Part** | **Description** | **Weight** |\n", "|----------|-------------------------------|------------|\n", "| 1 | Linear Regression | **40%** |\n", "| 2 | Logistic Regression | **40%** |\n", "| 3 | Knowledge Check | **20%** |\n", "\n", "\n", "## Submission Instructions\n", "\n", "To complete your submission for this homework, please follow these steps:\n", "\n", "1. **Save the Completed Notebook**: Ensure that all your code and written answers are finalized, then save the `.ipynb` file.\n", "2. **Export to PDF**: Additionally, export or save your completed notebook as a `PDF` file.\n", "3. **Submit Both Files**: Turn in both the `.ipynb` file and the `PDF` file. Make sure that both files are clearly labeled and include your Net ID (`HW_x_NetID`)." ] }, { "cell_type": "markdown", "id": "672a15df-3d28-4a2b-973a-8746b2696e34", "metadata": {}, "source": [ "Good luck and enjoy exploring Linear and Logistic Regression!\n", "\n", "## Missing libraries?\n", "\n", "Uncomment and run the cell for any of the libraries you are missing." ] }, { "cell_type": "markdown", "id": "1784669b-6c2e-48c4-a989-537bd6d83b9a", "metadata": {}, "source": [ "**NumPy**" ] }, { "cell_type": "code", "execution_count": null, "id": "0515edd5-d8e7-4319-99ac-e83a8b8eb12b", "metadata": {}, "outputs": [], "source": [ "#!pip install numpy" ] }, { "cell_type": "markdown", "id": "48e909e9-8b1f-4807-98b1-042fe539e68e", "metadata": {}, "source": [ "**pandas**" ] }, { "cell_type": "code", "execution_count": null, "id": "0ca4050d-ddb0-44b9-bccc-d38f0cd472d1", "metadata": {}, "outputs": [], "source": [ "#!pip install pandas" ] }, { "cell_type": "markdown", "id": "6764f163-444b-4131-bc97-e7aa17792a9c", "metadata": {}, "source": [ "**Matplotlib**" ] }, { "cell_type": "code", "execution_count": null, "id": "6ee439fe-ebc3-44ff-9a45-5230eab7f768", "metadata": {}, "outputs": [], "source": [ "#!pip install matplotlib" ] }, { "cell_type": "markdown", "id": "5982011f-9e8a-4621-8943-d8f0db3e5e28", "metadata": {}, "source": [ "**SciPy**" ] }, { "cell_type": "code", "execution_count": null, "id": "55fb1311-b447-4f96-84ab-14bee9a46ed8", "metadata": {}, "outputs": [], "source": [ "#!pip install scipy" ] }, { "cell_type": "markdown", "id": "96fd89ad-fe30-4f95-b180-ee13ed6c4155", "metadata": {}, "source": [ "**Scikit-Learn**" ] }, { "cell_type": "code", "execution_count": null, "id": "19a96f7c-1ca0-4e1e-b72b-844c8ed6d2c0", "metadata": {}, "outputs": [], "source": [ "#!pip install scikit-learn" ] }, { "cell_type": "markdown", "id": "32e39764-74ba-4745-a972-9b53e636af5c", "metadata": {}, "source": [ "**Seaborn**" ] }, { "cell_type": "code", "execution_count": null, "id": "96024d5b-a23c-404a-82a0-b302cbcf9e8d", "metadata": {}, "outputs": [], "source": [ "#!pip install seaborn" ] }, { "cell_type": "markdown", "id": "a3c65c68-8d5f-44e3-8022-097b74fc0626", "metadata": {}, "source": [ "\n", "# **Part 1: Linear Regression**\n", "\n", "### Worth: 40%" ] }, { "cell_type": "markdown", "id": "4bfe8d6e-573a-45ae-a5f9-8c6cb8f5135a", "metadata": {}, "source": [ "### Introduction to Linear Regression\n", "Linear Regression is a foundational statistical method that allows us to model relationships between variables. In this lab, we'll explore how to build a simple linear regression model that predicts house prices based on house sizes.\n", "\n", "\n", "### 1.1. Setting up the Environment\n", "Let's load the necessary libraries and set up our environment for the rest of the lab.\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "01c1c83f-d00e-421c-8c05-d4b363850b6b", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from scipy.optimize import minimize" ] }, { "cell_type": "markdown", "id": "a4dc73cc-c53d-434a-82b1-fb89678d21ce", "metadata": {}, "source": [ "\n", "### 1.2. Dataset Loading and Visualization\n", "We will begin by loading our dataset and visualizing the relationship between house size and price." ] }, { "cell_type": "code", "execution_count": 19, "id": "bb90feb9-6198-4ee2-9315-bd09e7053fbf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of Tennessee properties after cleaning: 2640\n", "Columns in the dataset: ['brokered_by', 'status', 'price', 'bed', 'bath', 'acre_lot', 'street', 'city', 'state', 'zip_code', 'house_size', 'prev_sold_date']\n", " brokered_by price bed bath acre_lot \\\n", "count 2640.000000 2.640000e+03 2640.000000 2640.000000 2454.000000 \n", "mean 68239.768561 4.055739e+05 3.196212 2.570455 0.793223 \n", "std 28513.014238 3.263594e+05 0.894250 1.044101 6.673927 \n", "min 102.000000 4.490000e+04 1.000000 1.000000 0.010000 \n", "25% 50640.500000 2.379000e+05 3.000000 2.000000 0.160000 \n", "50% 83146.000000 3.249000e+05 3.000000 2.000000 0.250000 \n", "75% 88990.000000 4.799000e+05 4.000000 3.000000 0.440000 \n", "max 109987.000000 5.200000e+06 8.000000 10.000000 306.830000 \n", "\n", " street zip_code house_size \n", "count 2.640000e+03 2640.000000 2640.000000 \n", "mean 1.057099e+06 37922.095076 2075.400000 \n", "std 5.741161e+05 7.419471 1086.973793 \n", "min 1.952900e+04 37902.000000 336.000000 \n", "25% 5.332722e+05 37918.000000 1320.000000 \n", "50% 1.192176e+06 37921.000000 1820.500000 \n", "75% 1.578688e+06 37931.000000 2589.000000 \n", "max 1.996419e+06 37938.000000 14093.000000 \n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Load the dataset\n", "url = \"https://media.githubusercontent.com/media/CuriousNeuralNerd/data/main/housing_market.csv\"\n", "data = pd.read_csv(url)\n", "\n", "# Clean and filter the data for Tennessee\n", "data['price'] = pd.to_numeric(data['price'])\n", "data['house_size'] = pd.to_numeric(data['house_size'])\n", "data['bed'] = pd.to_numeric(data['bed'])\n", "data['bath'] = pd.to_numeric(data['bath'])\n", "\n", "# Filter for Tennessee and remove rows with NA values\n", "data = data[(data['state'] == 'Tennessee') & (data['city'] == 'Knoxville')].dropna(subset=['price', 'house_size', 'bed', 'bath'])\n", "\n", "print(f\"Number of Tennessee properties after cleaning: {len(data)}\")\n", "print(f\"Columns in the dataset: {data.columns.tolist()}\")\n", "print(data.describe())\n", "\n", "plt.figure(figsize=(12, 8))\n", "plt.scatter(data['house_size'], data['price'], color='#ff8200', alpha=0.5)\n", "plt.title('1.2: Knoxville House Prices vs. Size')\n", "plt.xlabel('House Size (sq ft)')\n", "plt.ylabel('Price ($)')\n", "plt.show()\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "b6cd7004-ae69-43cc-b20a-ea22de98b1e3", "metadata": {}, "source": [ "### Theory Behind Linear Regression\n", "In linear regression, we predict the target variable using a linear formula based on the input features. The linear formula used is:\n", " `y = mx + b` where `m` is the slope and `b` is the intercept." ] }, { "cell_type": "markdown", "id": "8d32d53a-062b-4669-b485-f3f390d9daed", "metadata": {}, "source": [ "\n", "### 1.3. Implementation of the Cost Function\n", "\n", "The cost function helps us measure how well our model is performing. We use the Mean Squared Error (MSE) cost function which calculates the average of the squares of the errors—that is, the average squared difference between the estimated values and actual value.\n", "\n", "$MSE= \\frac{1}{n} ∑^n_{i=1}(y_i-ŷ_i)^2 $\n", "\n", "**Where:**\n", "\n", "- $MSE$ - Mean Squared Error\n", "- $n$ - Number of samples in the dataset\n", "- $yᵢ$ - Predicted value for the i-th sample\n", "- $ŷᵢ$ - Target value for the i-th sample\n", "- $Σ$ - Summation symbol" ] }, { "cell_type": "code", "execution_count": 3, "id": "643f4d36-973d-4e1d-9838-2a8f468da087", "metadata": {}, "outputs": [], "source": [ "def compute_cost(X, y, params):\n", " n_samples = len(y)\n", " h = X.dot(params)\n", " return (1/(2*n_samples)) * np.sum((h - y)**2)" ] }, { "cell_type": "markdown", "id": "b21368df-41a4-47c6-ac75-4279844c0fdd", "metadata": {}, "source": [ "\n", "## 1.4. Manual Adjustment of Parameters\n", "\n", "Before we dive into calculating the optimal parameters, let's develop an intuition for how different parameter values affect our model. In this exercise, you'll manually adjust the values of m (slope) and b (intercept) to see how they impact the cost and the linear fit.\n", "\n", "**Task (You):** \n", "15 points\n", "\n", "1. Manually Set the Values of $m$ (slope) and $b$ (intercept):\n", "\n", " - Assign specific values to $m$ and $b$ to understand how these parameters affect the cost function and the linear fit.\n", "

\n", "\n", "2. Compute the Corresponding Cost:\n", "\n", " - Use the manually set values of $m$ and $b$ to compute the cost associated with these parameters. This will give insight into how well these parameters fit the data.\n", "

\n", "\n", "3. Add comments to your code:\n", "\n", " - Ensure your code is well-commented to explain each step, making it understandable to anyone who reads it.\n", "

\n", "\n", "4. Variables you must use:\n", "\n", " - m: Slope of the line.\n", " - b: Intercept of the line.\n", " - params: A numpy array that includes both b and m.\n", " - cost: The calculated cost for the manually set parameters." ] }, { "cell_type": "markdown", "id": "340a5640-7cee-434f-bc4c-e166d25964fc", "metadata": {}, "source": [ "
\n", " Task Hint\n", "\n", "For m (slope), try values between 100 and 300. Remember, this represents the change in price for each unit increase in house size.\n", "\n", "- After trying a few different values set this value to 100 before moving on.\n", "\n", " \n", "For b (intercept), consider values between 0 and 200,000. This represents the baseline price when the house size is zero.\n", "\n", "- After trying a few different values set this value to 50000 before moving on.\n", "\n", "\n", "```python\n", "'''\n", "Lines of code ≈ 8\n", "'''\n", "# Create the feature matrix X with a column of ones for the intercept and house_size as the feature\n", "X = np.c_[np.ones(data.shape[0]), data['house_size']]\n", "\n", "# Extract the target variable y, which is the price of the houses\n", "y = data['price'].values\n", "\n", "# slope (m)\n", "m =\n", "\n", "# intercept (b)\n", "b =\n", "\n", "# Create an array of parameters to use in the cost calculation\n", "params =\n", "\n", "# Compute the cost and round to 2 decimal places\n", "cost =\n", "\n", "# Display the results of the manual parameter adjustment\n", "print(\"Manual adjustment cost:\", cost)\n", "print(\"Manual adjustment parameters: m =\", m, \", b =\", b)\n", "```" ] }, { "cell_type": "code", "execution_count": 4, "id": "283e0438-391f-43cf-a4fc-0fe1ab7b17a0", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-c2eb3ecf108974f4", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Manual adjustment cost: 40764452431.97\n", "Manual adjustment parameters: m = 100 , b = 50000\n" ] } ], "source": [ "# Create the feature matrix X with a column of ones for the intercept and house_size as the feature\n", "X = np.c_[np.ones(data.shape[0]), data['house_size']]\n", "\n", "# Extract the target variable y, which is the price of the houses\n", "y = data['price'].values\n", "\n", "### BEGIN SOLUTION\n", "# slope (m)\n", "m = 100\n", "\n", "# intercept (b)\n", "b = 50000 \n", "\n", "# Create an array of parameters to use in the cost calculation\n", "params = np.array([b, m])\n", "\n", "# Compute the cost\n", "cost = round(compute_cost(X, y, params),2)\n", "### END SOLUTION\n", "\n", "# Display the results of the manual parameter adjustment\n", "print(\"Manual adjustment cost:\", cost)\n", "print(\"Manual adjustment parameters: m =\", m, \", b =\", b)" ] }, { "attachments": { "cffb7036-7344-4392-9d01-f959184c192f.png": { "image/png": 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} }, "cell_type": "markdown", "id": "a798414e-10e8-4b7e-8e89-768f76afff08", "metadata": {}, "source": [ "
\n", " Expected Hint\n", "\n", "\n", "![image.png](attachment:cffb7036-7344-4392-9d01-f959184c192f.png)" ] }, { "cell_type": "markdown", "id": "174c411e-1fbe-411d-ac1f-2ac5b2b52808", "metadata": {}, "source": [ "\n", "### 1.5. Estimating Optimal Parameters\n", "\n", "Now that you have developed an intuition for how the parameters affect the model, let's calculate the optimal parameters using the Ordinary Least Squares (OLS) method.\n", "\n", "**Task (You):**\n", "15 points\n", "\n", "\n", "1. Compute the Optimal Parameters:\n", "\n", " - Use the Ordinary Least Squares (OLS) technique to compute the optimal parameters that minimize the sum of squared residuals.\n", " - The OLS method finds the best-fitting line through the data points by calculating the parameters that result in the lowest cost.\n", "

\n", "\n", "2. Round the Results:\n", "\n", " - Round the computed parameters and cost to 2 significant figures to ensure clarity and precision.\n", "

\n", "\n", "3. Add comments to your code:\n", "\n", " - Ensure your code is well-commented to explain each step, making it understandable to anyone who reads it.\n", "

\n", "\n", "4. Variables you must use:\n", "\n", " - theta_best: The vector of optimal parameters.\n", " - m_opt: The optimal slope of the line.\n", " - b_opt: The optimal intercept of the line.\n", " - params_opt: A numpy array containing the intercept and slope.\n", " - cost_opt: The computed cost for the optimal parameters." ] }, { "cell_type": "markdown", "id": "dada5305-5535-4f89-ad2d-b30effb2660e", "metadata": {}, "source": [ "
\n", " Task Hint\n", "\n", "The OLS formula for a simple linear regression is:\n", "\n", "$β = (X^T X)^{-1} X^T y$\n", "\n", "Where:\n", "\n", "- $β$ is the vector of parameters [b, m]\n", "- $X$ is the design matrix (including a column of 1s for the intercept)\n", "- $y$ is the target variable (price)\n", "- $^T$ denotes the transpose of a matrix\n", "- $^{(-1)}$ denotes the inverse of a matrix\n", " \n", "\n", "1. Create the design matrix X by adding a column of 1s to the house_size data.\n", "2. Extract the target variable y (price).\n", "3. Use numpy's linear algebra functions to compute β according to the OLS formula.\n", "4. Extract the optimal values for m (slope) and b (intercept) from β.\n", "5. Calculate the cost using these optimal parameters.\n", "\n", "\n", "```python\n", "''' \n", "Lines of code ≈ 7\n", "'''\n", "# The formula used is theta_best = (X^T * X)^-1 * X^T * y\n", "theta_best =\n", "\n", "# Extract and round the optimal slope (m_opt) to 2 significant figures\n", "m_opt =\n", "\n", "# Extract and round the optimal intercept (b_opt) to 2 significant figures\n", "b_opt =\n", "\n", "# Store the optimal parameters (intercept and slope) in an array\n", "params_opt =\n", "\n", "# Compute the cost for the optimal parameters using the cost function\n", "cost_opt =\n", "```" ] }, { "cell_type": "code", "execution_count": 6, "id": "b21ab4d0-4ceb-49e7-950f-0760d042eb5b", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-1d3c82ff8a644424", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimal cost: 16809372878.16\n", "Optimal parameters: m = 248.36 , b = -109872.94\n" ] } ], "source": [ "### BEGIN SOLUTION\n", "# The formula used is theta_best = (X^T * X)^-1 * X^T * y\n", "theta_best = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(y)\n", "\n", "# Extract and round the optimal slope (m_opt) to 2 significant figures\n", "m_opt = round(theta_best[1], 2)\n", "\n", "# Extract and round the optimal intercept (b_opt) to 2 significant figures\n", "b_opt = round(theta_best[0], 2)\n", "\n", "# Store the optimal parameters (intercept and slope) in an array\n", "params_opt = np.array([b_opt, m_opt])\n", "\n", "# Compute the cost for the optimal parameters using the cost function\n", "cost_opt = round(compute_cost(X, y, params_opt), 2)\n", "### END SOLUTION\n", "\n", "# Display the computed cost and optimal parameters\n", "print(\"Optimal cost:\", cost_opt)\n", "print(\"Optimal parameters: m =\", m_opt, \", b =\", b_opt)" ] }, { "attachments": { "67b54439-59b9-4207-9650-7176f9a06ee1.png": { "image/png": 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} }, "cell_type": "markdown", "id": "85315f1a-0afe-4680-a1af-254cbcac7670", "metadata": {}, "source": [ "
\n", " Expected Hint\n", "\n", "![image.png](attachment:67b54439-59b9-4207-9650-7176f9a06ee1.png)" ] }, { "cell_type": "markdown", "id": "12eadca1-44dd-46d4-ade5-9269219067ca", "metadata": {}, "source": [ "\n", "## 1.6. Visualizing the Linear Fit\n", "\n", "Let's plot the linear fit along with the data points.\n", "\n", "#### (You)\n", "\n", "**Task:** \n", "10 points\n", "\n", "1. Create a Scatter Plot:\n", "\n", " - Plot the house prices against house sizes using a scatter plot.\n", " - Store the scatter plot data in a variable.\n", "

\n", "\n", "2. Calculate Predicted Values:\n", "\n", " - Complete the code to calculate the predicted values (`y_pred_opt`) using the optimal parameters.\n", " - Also, calculate the predicted values (`y_pred_manual`) using manually adjusted parameters.\n", "

\n", "\n", "3. Plot Predictions:\n", "\n", " - Plot the predicted house prices along with the actual data points.\n", " - Use different colors to distinguish between the predictions using the optimal parameters and the manually adjusted parameters.\n", "

\n", "\n", "4. Add comments to your code:\n", "\n", " - Ensure your code is well-commented to explain each step, making it understandable to anyone who reads it.\n", "

\n", "\n", "5. Variables you must use:\n", " - scatter_data\n", " - y_pred_opt\n", " - y_pred_manual" ] }, { "cell_type": "markdown", "id": "ff399b00-b6f4-4f5f-bcde-0f0636ae26cb", "metadata": {}, "source": [ "
\n", " Task Hint\n", "\n", "```python\n", "'''\n", "Lines of code ≈ 15\n", "'''\n", "\n", "# Store the scatter plot data in a dictionary\n", "scatter_data =\n", "\n", "# Calculate the predicted values using the optimal parameters\n", "y_pred_opt =\n", "\n", "# Calculate the predicted values using manually adjusted parameters\n", "y_pred_manual =\n", "\n", "# Create a plot to visualize the data\n", "\n", "# Scatter plot for the actual house sizes and prices\n", "\n", "# Plot for the predicted values using optimal parameters\n", "\n", "# Plot for the predicted values using manually adjusted parameters\n", "\n", "# Add labels and title to the plot\n", "\n", "\n", "\n", "# Display the legend to differentiate between the plots\n", "\n", "# Show the plot\n", "\n", "```" ] }, { "cell_type": "code", "execution_count": 18, "id": "ee528464-5ff7-4e62-b743-c1d8fc7a5c51", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-9e90e5e401704494", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### BEGIN SOLUTION\n", "# Store the scatter plot data in a dictionary\n", "scatter_data = {\n", " 'house_size': data['house_size'],\n", " 'price': data['price']\n", "}\n", "\n", "# Calculate the predicted values using the optimal parameters\n", "y_pred_opt = X.dot(params_opt)\n", "\n", "# Calculate the predicted values using manually adjusted parameters\n", "y_pred_manual = X.dot(params)\n", "\n", "# Create a plot to visualize the data\n", "plt.figure(figsize=(10, 6))\n", "\n", "# Scatter plot for the actual house sizes and prices\n", "plt.scatter(data['house_size'], data['price'], label='Data points', color='#ff8200', alpha=0.5)\n", "\n", "# Plot for the predicted values using optimal parameters\n", "plt.plot(data['house_size'], y_pred_opt, color='green', label='Optimal fit')\n", "\n", "# Plot for the predicted values using manually adjusted parameters\n", "plt.plot(data['house_size'], y_pred_manual, color='red', label='Manual fit')\n", "\n", "# Add labels and title to the plot\n", "plt.title('1.6: House Prices vs. Size with Linear Fit')\n", "plt.xlabel('House Size (sq ft)')\n", "plt.ylabel('Price')\n", "\n", "# Display the legend to differentiate between the plots\n", "plt.legend()\n", "\n", "# Show the plot\n", "plt.show()\n", "### END SOLUTION" ] }, { "attachments": { "2da29e20-9b68-4d4e-9604-5baeb2df3b1a.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "c321f4e3-e7c6-4a13-86e3-aafd5f1c69c1", "metadata": {}, "source": [ "
\n", " Expected Hint\n", "\n", "\n", "![image.png](attachment:2da29e20-9b68-4d4e-9604-5baeb2df3b1a.png)\n", "\n", "**Explanation:**\n", "\n", "- Scatter Plot Data: The `scatter_data` dictionary is used to store the actual house sizes and prices.\n", "- Predicted Values: `y_pred_opt` and `y_pred_manual` are used to store the predicted prices using the optimal and manually adjusted parameters, respectively.\n", "- Plotting: A scatter plot is created to show the actual data points. Two line plots are added to show the predictions using optimal and manually adjusted parameters, which helps visualize how well each model fits the data." ] }, { "cell_type": "markdown", "id": "f43beb63-69d9-4d8a-989e-ef62209e2042", "metadata": {}, "source": [ "\n", "# **Part 2: Logistic Regression**\n", "\n", "### Worth: 40%" ] }, { "cell_type": "markdown", "id": "401fa32d-ae00-4e23-8bae-67150e086bbc", "metadata": {}, "source": [ "## Introduction to Logistic Regression\n", "\n", "Logistic Regression is a popular classification algorithm used to predict the probability of a binary outcome based on one or more predictor variables. In this lab, we'll build a simple logistic regression model." ] }, { "cell_type": "markdown", "id": "fe81d006-01b7-40f1-8c9c-009b5dda3bfd", "metadata": {}, "source": [ "\n", "## 2.1. Setting up the Environment\n", "\n", "Let's load the necessary libraries and set up our environment for the rest of the lab." ] }, { "cell_type": "code", "execution_count": 8, "id": "0715d372-c28e-4c3f-83ba-fc3870625d6f", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report\n", "from scipy.optimize import minimize\n", "from matplotlib.colors import LinearSegmentedColormap\n", "\n", "# Load the dataset\n", "url = \"https://media.githubusercontent.com/media/CuriousNeuralNerd/data/main/housing_market.csv\"\n", "data = pd.read_csv(url)" ] }, { "cell_type": "markdown", "id": "892f382c-9b10-4530-9b21-664d038e48f1", "metadata": {}, "source": [ "\n", "## 2.2. Dataset Loading and Visualization\n", "\n", "We will begin by cleaning and preprocessing our dataset, followed by visualizing the relationship between house size and the number of bathrooms." ] }, { "cell_type": "code", "execution_count": 20, "id": "1fd3ccf3-d86e-4fdc-b8a9-52e4d157f62d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of houses with more than 2 bathrooms: 1250\n", "Number of houses with 2 or fewer bathrooms: 1204\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Clean and preprocess the data\n", "data['price'] = pd.to_numeric(data['price'])\n", "data['house_size'] = pd.to_numeric(data['house_size'])\n", "data['bed'] = pd.to_numeric(data['bed'])\n", "data['bath'] = pd.to_numeric(data['bath'])\n", "data['acre_lot'] = pd.to_numeric(data['acre_lot'])\n", "\n", "# Filter for Knoxville and remove rows with missing values\n", "data = data[(data['state'] == 'Tennessee') & (data['city'] == 'Knoxville')].dropna(subset=['price', 'house_size', 'bed', 'bath', 'acre_lot'])\n", "\n", "# Binary classification: 2 or fewer bathrooms (0) or more than 2 bathrooms (1)\n", "data['bathroom_class'] = (data['bath'] > 2.0).astype(int)\n", "\n", "print(f\"Number of houses with more than 2 bathrooms: {sum(data['bathroom_class'])}\")\n", "print(f\"Number of houses with 2 or fewer bathrooms: {len(data) - sum(data['bathroom_class'])}\")\n", "\n", "plt.figure(figsize=(12, 8))\n", "plt.scatter(data['house_size'], data['bath'], c=data['bathroom_class'], cmap='coolwarm', alpha=0.5)\n", "plt.xlabel('House Size (sq ft)')\n", "plt.ylabel('Number of Bathrooms')\n", "plt.title('2.2: Knoxville Houses: Bathrooms vs Size')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "5878e0c7-d8b5-4c48-bc56-eb3a8237de08", "metadata": {}, "source": [ "## Theory Behind Logistic Regression\n", "\n", "Logistic regression predicts the probability of the outcome being 1 or 0 using the logistic function (sigmoid function):\n", "\n", "$$\\hat{y} = h_θ(x) = \\frac{1}{1 + e^{-θ^T x}} = \\frac{1}{1 + e^{-z}}$$\n", "\n", "**Where:**\n", "\n", "- $h_𝜃(x)$ (also denoted as $\\hat{y}$) represents the hypothesis function, which outputs the predicted probability that the target variable $Y$ equals 1 given the inputs $x$.\n", "- $𝜃$ is the parameter vector including the bias $𝜃_0$ and weights $𝜃_1,𝜃_2,...,𝜃_n$ corresponding to the features.\n", "- $x$ represents the feature vector, including $x_1,x_2,...,x_n$\n", "- $𝜃^Tx$ denotes the dot product of the vectors $𝜃$ and $x$, which results in a scalar value. This scalar is the input to the sigmoid function.\n", "\n", "**Decision Boundary:**\n", "\n", "- Logistic regression uses the hypothesis $h_θ(x)$ or $\\hat{y}$ to estimate the probability that $Y=1$ for given $x$.\n", "- To convert this probability into a binary outcome (0 or 1), a threshold (typically 0.5) is applied:\n", " - If $\\hat{y}≥0.5$, the prediction is $Y=1$.\n", " - If $\\hat{y}<0.5$, the prediction is $Y=0$." ] }, { "cell_type": "markdown", "id": "a97ca0a9-74d3-4cb3-9031-f72963e86ac8", "metadata": {}, "source": [ "\n", "## 2.3 Splitting the Data and Explanation of Features\n", "\n", "In this part of the exercise, we will prepare the feature matrix `X` and the target variable `y`. The feature matrix `X` will consist of only one feature: `house_size`. This is a deliberate choice to simplify the learning process and focus on understanding the logistic regression model with a single predictor. Later in the semester, we will explore logistic regression with multiple features, which will allow us to model more complex relationships in the data.\n", "\n", "#### (You)\n", "\n", "**Task:** points\n", "\n", "1. **Create the Feature Matrix (X):**\n", "\n", " - Add a column of ones to the feature house_size to account for the intercept term in logistic regression.\n", " - This means you will create a new matrix X where the first column is all ones (for the intercept), and the second column is the house_size feature.\n", " \n", "

\n", "\n", "2. **Extract the Target Variable (y):**\n", "\n", " - The target variable for this task is bathroom_class, which indicates whether a house has 2 or fewer bathrooms (0) or more than 2 bathrooms (1).\n", "\n", "

\n", "\n", "3. **Split the Data:**\n", "\n", " - Use the train_test_split function to split the data into training and test sets.\n", " - Allocate 80% of the data to the training set and 20% to the test set.\n", " - Make sure to set the random_state parameter to 42 for reproducibility of results.\n", "\n", "

\n", "\n", "4. **Output the Number of Samples:**\n", "\n", " - Display the number of samples in each of the sets: `X_train`, `X_test`, `y_train`, and `y_test`.\n", " - This will help verify that the data split has been performed correctly." ] }, { "cell_type": "markdown", "id": "a87df33c-4ece-4022-bb00-8a46f99c7e03", "metadata": {}, "source": [ "
\n", " Task Hint\n", "\n", "\n", "```python\n", "'''\n", "Lines of code ≈ 7 \n", "'''\n", "\n", "# Create feature matrix X including only house_size with an added column of ones for the intercept\n", "X = \n", "\n", "# Extract the target variable y\n", "y = \n", "\n", "# Split the data into training and test sets (80% training, 20% testing)\n", "X_train, X_test, y_train, y_test =\n", "\n", "# Display the number of samples in each set\n", "\n", "```" ] }, { "cell_type": "code", "execution_count": 10, "id": "3080ba83-aa4c-468e-9d50-651aaca262ce", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-f2b49c316885a246", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of samples in X_train: 1963\n", "Number of samples in X_test: 491\n", "Number of samples in y_train: 1963\n", "Number of samples in y_test: 491\n" ] } ], "source": [ "### BEGIN SOLUTION\n", "# Create feature matrix X including only house_size with an added column of ones for the intercept\n", "X = np.c_[np.ones(data.shape[0]), data['house_size']]\n", "\n", "# Extract the target variable y\n", "y = data['bathroom_class'].values\n", "\n", "# Split the data into training and test sets (80% training, 20% testing)\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", "\n", "# Display the number of samples in each set\n", "print(f\"Number of samples in X_train: {X_train.shape[0]}\")\n", "print(f\"Number of samples in X_test: {X_test.shape[0]}\")\n", "print(f\"Number of samples in y_train: {y_train.shape[0]}\")\n", "print(f\"Number of samples in y_test: {y_test.shape[0]}\")\n", "### END SOLUTION" ] }, { "cell_type": "markdown", "id": "a9c6651c-7b20-40ac-afc9-a0150da43dbf", "metadata": {}, "source": [ "\n", "## 2.4. Implementation of the Sigmoid Function\n", "\n", "The sigmoid function maps any real number to the (0, 1) interval, making it suitable for modeling probabilities.\n", "\n", "**Task:**\n", "\n", "1. Define the Sigmoid Function:\n", "\n", " - Implement a Python function named `sigmoid` that takes an input `z` and returns the sigmoid of `z`.\n", " - The sigmoid function is defined as $σ(z)= \\frac{1}{1+e^{−z}}$\n", "\n", "

\n", " \n", "2. Generate Input Values:\n", "\n", " - Create a range of values from -10 to 10 using `np.linspace` to simulate a range of possible inputs to the sigmoid function.\n", "\n", "

\n", "\n", "3. Calculate Sigmoid Values:\n", "\n", " - Apply the `sigmoid` function to the range of values created.\n", "\n", "

\n", "\n", "4. Visualize the Sigmoid Function:\n", "\n", " - Plot the sigmoid function using the input values and the calculated sigmoid values." ] }, { "cell_type": "code", "execution_count": 21, "id": "4db027e6-b465-4683-97da-b39c0dcc020f", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def sigmoid(z):\n", " return 1 / (1 + np.exp(-z))\n", "\n", "# Example usage:\n", "z = np.linspace(-10, 10, 100)\n", "sigmoid_values = sigmoid(z)\n", "\n", "# Plotting the sigmoid function\n", "plt.plot(z, sigmoid_values)\n", "plt.title('2.4: Sigmoid Function')\n", "plt.xlabel('z')\n", "plt.ylabel('sigmoid(z)')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "3f596925-d5d0-4898-8a66-0921007872d7", "metadata": {}, "source": [ "\n", "## 2.5. Implementation of the Cost Function\n", "\n", "The cost function for logistic regression is given by:\n", "\n", "$$J(θ)=− \\frac{1}{m} ∑^m_{i=1}[y^{(i)}log(h_θ(x^{(i)}))+(1-y^{(i)})log(1-h_θ(x^{(i)}))] $$\n", "\n", "\n", "**Where:**\n", "\n", "- $J(θ)$ is the cost function evaluated using the parameter vector $θ$.\n", "- $m$ is the number of training examples.\n", "- $y^{(i)}$ is the actual label of the $𝑖$-th training example\n", "- $x^{(i)}$ is the feature vector of the $𝑖$-th training example.\n", "- $h_θ(x^{(i)})$ is the hypothesis function defined as $𝜎(θ^Tx^{(i)})$ where $𝜎(z)$ is the logistic function or sigmoid function, given by:\n", " $$𝜎(z)= \\frac{1}{1 + e^{-z}}$$\n", "\n", "**or** \n", "\n", "\n", "$$J(\\theta) = -\\frac{1}{m} \\sum_{i=1}^{m} \\left[ y^{(i)} \\log(\\hat{y}^{(i)}) + (1 - y^{(i)}) \\log(1 - \\hat{y}^{(i)}) \\right]$$\n", "\n", "\n", "**Where:**\n", "\n", "- $J(θ)$ is the logistic regression cost function.\n", "- $m$ is the number of training examples.\n", "- $y^{(i)}$ is the actual label of the i-th training example, where $y^{(i)}$ can be 0 or 1.\n", "- $\\hat{y}^{(i)}$ is the predicted probability that the label $y^{(i)}$ is 1, given the input $x^{(i)}$ This predicted probability is calculated using the sigmoid function:\n", "$$\\hat{y}^{(i)} = \\frac{1}{1 + e^{-\\theta^T x^{(i)}}}$$\n", "\n", "\n", "**Full Explanation:**\n", "\n", "- $y^{(i)} log(\\hat{y}^{(i)})$: This term penalizes the model if the actual label $y^{(i)}$ is 1 but the predicted probability $\\hat{y}^{(i)}$ is low. If the model confidently predicts a 1 and the actual value is 1, this term will contribute very little to the cost.\n", "\n", "- $(1 - y^{(i)}) \\log(1 - \\hat{y}^{(i)})$: This term penalizes the model if the actual label $y^{(i)}$ is 0 but the predicted probability $\\hat{y}^{(i)}$ is high. If the model confidently predicts a 0 and the actual value is 0, this term will contribute very little to the cost." ] }, { "cell_type": "markdown", "id": "1c67b2e8-1cdf-46b6-95e7-ad28970e7752", "metadata": {}, "source": [ "
\n", " Task Hint\n", "\n", "\n", "```python\n", "'''\n", " Lines of code ≈ 5 \n", "\n", " Compute the logistic regression cost function.\n", " \n", " Parameters:\n", " y (numpy array): The actual labels.\n", " y_hat (numpy array): The predicted probabilities.\n", "\n", " Returns:\n", " cost (float): The logistic regression cost.\n", "'''\n", "```" ] }, { "cell_type": "code", "execution_count": 12, "id": "6c496a70-54ab-49b9-b4dc-484cf0bb3377", "metadata": {}, "outputs": [], "source": [ "def compute_cost_logistic(y, y_hat):\n", " m = len(y) # Number of training examples\n", " epsilon = 1e-5 # Small constant to prevent log being 0\n", " # Compute the logistic regression cost\n", " cost = (-1 / m) * (y.T.dot(np.log(y_hat + epsilon)) + (1 - y).T.dot(np.log(1 - y_hat + epsilon)))\n", " return cost" ] }, { "cell_type": "markdown", "id": "b2eeec58-aefa-4e5a-8640-05084f9829e3", "metadata": {}, "source": [ "\n", "## 2.6. Cost Calculation for Logistic Regression\n", "\n", "In this exercise, you'll calculate the initial cost of our logistic regression model using the logistic regression cost function.\n", "\n", "\n", "#### (You)\n", "\n", "**Task:** \n", "10 points\n", "\n", "1. Initialize Parameters:\n", "\n", " - Create an initial `theta` array with zeros, having a size equal to the number of features in `X_train`.\n", "\n", "

\n", "\n", "2. Calculate Predictions (`y_hat`):\n", "\n", " - Use the sigmoid function to calculate the predicted probabilities for the training set using the initial `theta`.\n", "\n", "

\n", "\n", "3. Calculate Initial Cost:\n", "\n", " - Use the `compute_cost_logistic` function to calculate the cost of the model using the initial `theta` and the calculated `y_hat` for the training set.\n", " - Round the result to two decimal places and store it in a variable named `cost_logistic_train`.\n", "\n", "

\n", "\n", "4. Print the Cost:\n", "\n", " - Display the cost to understand how well the model is performing.\n", " \n", "

\n", "\n", "5. Add comments to your code:\n", " \n", " - Write comments above your code to ensure that anyone unfamiliar with your code can easily understand what you are doing. \n", "

\n", "\n", "6. Variables to use:\n", " - initial_theta\n", " - y_hat_train\n", " - cost_logistic_train" ] }, { "cell_type": "markdown", "id": "09997085-ba13-43d4-a7ed-9ec8e475f313", "metadata": {}, "source": [ "
\n", " Task Hint\n", " \n", "\n", "Technique: Logistic Regression Cost Calculation\n", "\n", "In logistic regression, we use the logistic regression cost function to measure how well our current model parameters fit the training data. This function is already implemented in compute_cost_logistic. The key steps are:\n", "\n", "1. Prepare the Input Data (X_train and y_train): Make sure you have the training data ready.\n", "2. Initialize the Parameters theta: Create an initial array for theta filled with zeros. The size of theta should match the number of features in X_train.\n", "3. Calculate Predictions (y_hat_train): Use the sigmoid function to calculate the predicted probabilities for the training set using the initial theta.\n", "4. Calculate the Cost: Call the compute_cost_logistic function with the training data, theta, and the predicted values (y_hat_train). Round the result to two decimal places.\n", "5. Print the Initial Cost: Output the calculated cost to understand how well the model is performing initially.\n", "\n", "\n", "```python\n", "'''\n", "Lines of code ≈ 4\n", "'''\n", "\n", "# Initialize theta\n", "initial_theta =\n", "\n", "# Calculate y_hat using the initial theta values for the training set\n", "y_hat_train =\n", "\n", "# Compute the cost using the updated cost function for the training set\n", "cost_logistic_train =\n", "\n", "# Print the computed cost for the training set\n", "\n", "```" ] }, { "cell_type": "code", "execution_count": 13, "id": "97950de0-b110-422a-a454-6cd049b0ee8d", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-b1407aa0dd5cf42e", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cost (logistic) on training set: 0.69\n" ] } ], "source": [ "### BEGIN SOLUTION\n", "# Initialize theta\n", "initial_theta = np.zeros(X_train.shape[1])\n", "\n", "# Calculate y_hat using the initial theta values for the training set\n", "y_hat_train = sigmoid(X_train.dot(initial_theta))\n", "\n", "# Compute the cost using the updated cost function for the training set\n", "cost_logistic_train = round(compute_cost_logistic(y_train, y_hat_train), 2)\n", "\n", "# Print the computed cost for the training set\n", "print(\"Cost (logistic) on training set:\", cost_logistic_train)\n", "### END SOLUTION" ] }, { "attachments": { "ef1ec5ab-dc81-49e3-ba79-2937f6016b91.png": { "image/png": "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" } }, "cell_type": "markdown", "id": "64161997-fc1b-4740-bafb-0073c9e24889", "metadata": {}, "source": [ "
\n", " Expected Hint\n", "\n", "![image.png](attachment:ef1ec5ab-dc81-49e3-ba79-2937f6016b91.png)" ] }, { "cell_type": "markdown", "id": "73107df6-2d32-4ad0-9a95-e179c5add9c4", "metadata": {}, "source": [ "\n", "## 2.7. Optimizing Theta Parameters\n", "\n", "In this section, we use scipy's optimize function to find the best parameters for our logistic regression model.\n", "Explanation: The optimize_theta function does the following:\n", "\n", "1. Define the Optimization Function:\n", "\n", " - Implement a Python function named optimize_theta that takes theta, X, and y as inputs.\n", " - Use the minimize function from scipy.optimize to find the values of theta that minimize the logistic regression cost function.\n", "\n", "

\n", "\n", "2. Define the Objective Function:\n", "\n", " - Inside optimize_theta, define an inner function objective_function that calculates the cost using the current theta, X, and y.\n", "\n", "

\n", "\n", "3. Run the Optimization:\n", "\n", " - Use the minimize function with the objective_function, initial theta, and the method 'TNC' to perform the optimization.\n", "\n", "

\n", "\n", "4. Return the Results:\n", "\n", " - The function should return the optimized theta values and the minimized cost.\n", "\n", "This optimization process helps us find the best-fitting parameters for our logistic regression model, which will give us the most accurate predictions." ] }, { "cell_type": "markdown", "id": "15597469-ccfb-4561-81f0-36d89163cae9", "metadata": {}, "source": [ "
\n", " Task Hint\n", "\n", "```python\n", "'''\n", "This function optimizes theta parameters for logistic regression by minimizing the cost function.\n", "\n", " Parameters:\n", " theta (numpy array): Initial guess for the parameters.\n", " X (numpy array): Feature matrix.\n", " y (numpy array): Actual labels.\n", "\n", " Returns:\n", " result.x (numpy array): Optimized theta parameters.\n", " result.fun (float): The minimized cost function value.\n", "'''\n", "```" ] }, { "cell_type": "code", "execution_count": 14, "id": "db71a708-b153-439d-a2f9-eafbe12da1a9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimized cost (logistic) on training set: 0.32\n", "Optimized theta: [-7.48664306e+00 3.96942392e-03]\n" ] } ], "source": [ "from scipy.optimize import minimize\n", "\n", "# Optimization function\n", "def optimize_theta(theta, X, y):\n", "\n", " # Define the objective function to minimize\n", " def objective_function(t):\n", " # Calculate the predicted probabilities (y_hat)\n", " y_hat = sigmoid(X.dot(t))\n", " # Compute the cost with current theta, y, and y_hat\n", " return compute_cost_logistic(y, y_hat)\n", " \n", " # Run the optimizer with the objective function\n", " result = minimize(objective_function, theta, method='TNC', jac=False)\n", " return result.x, result.fun\n", "\n", "# Run the optimization to find the best theta using training data\n", "theta_optimized, cost_optimized_train = optimize_theta(initial_theta, X_train, y_train)\n", "\n", "print(\"Optimized cost (logistic) on training set:\", round(cost_optimized_train,2))\n", "print(\"Optimized theta:\", theta_optimized)" ] }, { "cell_type": "markdown", "id": "806fc4e9-1d6d-48a0-ab7e-e8a8ce45dd6e", "metadata": {}, "source": [ "\n", "## 2.8. Visualizing the Decision Boundary\n", "\n", "First, we will plot the sigmoid function applied to the test data points to visualize how the model predicts probabilities.\n", "\n", "\n", "#### (You)\n", "**Task:** \n", "10 points\n", "\n", "1. Plot the Sigmoid Function:\n", "\n", " - Generate a plot that shows the sigmoid function applied to a range of house sizes.\n", " - Include the actual test data points and the decision boundary based on the initial threshold of `0.5`.\n", "\n", "

\n", "\n", "2. Label the Axes and Title:\n", "\n", " - Label the x-axis as 'House Size (sq ft)' and the y-axis as 'Predicted Probability of Having More than 2 Bathrooms'.\n", " - Add a title to the plot to clearly describe the visualization.\n", "\n", "

\n", "\n", "3. Use an Initial Threshold:\n", "\n", " - Start with an initial threshold of `0.5`. This is a common choice that assumes equal probability for both classes.\n", "\n", "

\n", "\n", "4. Adjust the Threshold:\n", "\n", " - After using the initial threshold of `0.5`, experiment with different threshold values (e.g., `0.0`, `0.1`, `0.2`, ..., `1.0`) to observe how they impact the decision boundary and model predictions.\n", " - Identify the threshold that seems to provide the best separation between the classes.\n", "\n", "

\n", "\n", "5. Add comments to your code:\n", " \n", " - Write comments above your code to ensure that anyone unfamiliar with your code can easily understand what you are doing. \n", "

\n", "\n", "6. Variables to Use:\n", " - threshold\n", " - y_hat_test\n", " - house_sizes\n", " - z_values\n", " - sigmoid_values\n" ] }, { "cell_type": "markdown", "id": "9749e039-7476-4753-8b9f-f5c28550ddf4", "metadata": {}, "source": [ "
\n", " Task Hint\n", "\n", "1. The decision boundary is calculated based on the threshold for classification, which is typically set to 0.5. In logistic regression, this can be found using a formula that involves the model's parameters and the threshold. The decision boundary occurs where the probability equals the threshold, and this is computed as: $$𝑥=\\frac{log(\\frac{threshold}{1−threshold})-𝜃_0}{𝜃_1}$$\n", "2. You can calculate the x-coordinate of the decision boundary using: $$𝑥=\\frac{log(\\frac{threshold}{1−threshold})-𝜃_0}{𝜃_1}$$\n", "3. Use plt.axvline to plot a vertical line at this x-coordinate\n", "\n", "```python\n", "'''\n", "Lines of code ≈ 15\n", "'''\n", "# Set the threshold for classification\n", "threshold =\n", "\n", "# Predict on the test set using the optimized theta\n", "y_hat_test =\n", "\n", "# Plotting the sigmoid applied to the house sizes with test data points\n", "\n", "\n", "# Plot the actual data points from the test set\n", "\n", "\n", "# Generate a smooth range of house sizes for the sigmoid function (min and max ... 10000)\n", "house_sizes =\n", "\n", "# Compute the linear combination of features and optimized theta\n", "z_values =\n", "\n", "# Apply the sigmoid function to get the probabilities\n", "sigmoid_values =\n", "\n", "# Plot the sigmoid function\n", "```" ] }, { "cell_type": "code", "execution_count": 22, "id": "520811ce-345a-41b6-86d9-030791ad195e", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-28a992322c984380", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Where the boundary crosses the x axis: 1886.08\n" ] } ], "source": [ "### BEGIN SOLUTION\n", "# Set the threshold for classification\n", "threshold = 0.5\n", "\n", "# Predict on the test set using the optimized theta\n", "y_hat_test = sigmoid(X_test.dot(theta_optimized))\n", "\n", "# Plotting the sigmoid applied to the house sizes with test data points\n", "plt.figure(figsize=(12, 8))\n", "\n", "# Plot the actual data points from the test set\n", "plt.scatter(X_test[:, 1], y_test, c=y_test, cmap='coolwarm', alpha=0.6, label='Actual Data')\n", "\n", "# Generate a smooth range of house sizes for the sigmoid function\n", "house_sizes = np.linspace(data['house_size'].min(), data['house_size'].max(), 10000)\n", "\n", "# Compute the linear combination of features and optimized theta\n", "z_values = theta_optimized[0] + theta_optimized[1] * house_sizes\n", "\n", "# Apply the sigmoid function to get the probabilities\n", "sigmoid_values = sigmoid(z_values)\n", "### END SOLUTION\n", "\n", "# Plot the sigmoid function\n", "plt.plot(house_sizes, sigmoid_values, color='blue', label='Sigmoid Function')\n", "\n", "# Plot the decision boundary based on the threshold\n", "decision_boundary = (-np.log(threshold / (1 - threshold)) - theta_optimized[0]) / theta_optimized[1]\n", "plt.axvline(x=decision_boundary, color='green', linestyle='--', label=f'Decision Boundary (Threshold = {threshold})')\n", "\n", "plt.title('2.8: Sigmoid Function Applied to House Sizes')\n", "plt.xlabel('House Size (sq ft)')\n", "plt.ylabel('<=2 (0) or >2 (1)')\n", "plt.axhline(threshold, color='red', linestyle='--', label=f'Probability Threshold = {threshold}')\n", "plt.legend()\n", "plt.show()\n", "\n", "print(f'Where the boundary crosses the x axis: {decision_boundary:.2f}')\n" ] }, { "attachments": { "8376bcbd-65b7-4c05-af0e-3a9fa57499d0.png": { "image/png": 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} }, "cell_type": "markdown", "id": "340831d4-0c30-4d34-bc11-9309801cce68", "metadata": {}, "source": [ "
\n", " Expected Hint\n", "\n", "![image.png](attachment:8376bcbd-65b7-4c05-af0e-3a9fa57499d0.png)\n", "\n", "**Decision Boundary (Green Line)**\n", "\n", "- Definition: The decision boundary is a line or a hyperplane that separates the data points of different classes. In logistic regression, it's used to decide whether a data point belongs to class 0 or class 1 based on the feature(s) used. In this plot, the green line represents the decision boundary.\n", "\n", "- How It's Calculated: For a single-feature logistic regression model, the decision boundary is calculated based on the logistic regression equation and the chosen threshold. Mathematically, it's the point where the model predicts a 50% chance of being in class 1. In this case, it is calculated using the formula:\n", "\n", "$$𝑥=\\frac{log(\\frac{threshold}{1−threshold})-𝜃_0}{𝜃_1}$$\n", " \n", "Here, `theta_0` is the intercept, `theta_1` is the coefficient for the feature (house size), and the threshold is 0.5 by default. The decision boundary line is placed where the linear combination of the input feature(s) and the weights equals zero.\n", "\n", "- Interpretation: Any house size to the left of the green line (below the boundary) will be classified as having 2 or fewer bathrooms (class 0). Any house size to the right of the green line (above the boundary) will be classified as having more than 2 bathrooms (class 1). This line is crucial as it provides a visual cue for the separation between the two predicted classes.\n", "\n", "**Probability Threshold (Red Line)**\n", "\n", "- Definition: The probability threshold is a horizontal line that represents the cut-off probability at which the model switches from predicting one class to another. By default, the threshold is set to 0.5 in logistic regression, which means that if the predicted probability of the data point belonging to class 1 is greater than or equal to 0.5, it is classified as class 1; otherwise, it's classified as class 0.\n", "\n", "- How It's Represented: In the plot, the red line is drawn at y=0.5. This line shows the threshold probability. The logistic function (sigmoid curve) crosses this line, indicating the probability at which the model predicts class 1 or class 0.\n", "\n", "- Interpretation: Points above the red line indicate a higher probability of belonging to class 1 (more than 2 bathrooms), and points below indicate a higher probability of belonging to class 0 (2 or fewer bathrooms).\n", "\n", "**How They Work Together**\n", "\n", "- Decision Making: The green decision boundary line and the red probability threshold line work together to classify the data points. The decision boundary (green line) indicates where the input feature value (house size) changes the class prediction from 0 to 1 based on the threshold set by the red line (probability threshold).\n", "\n", "- Class Predictions: For house sizes:\n", "\n", " - To the left of the green line, the model predicts class 0 (<= 2 bathrooms) because the probability of being class 1 is less than 0.5.\n", " - To the right of the green line, the model predicts class 1 (> 2 bathrooms) because the probability is greater than 0.5.\n", "\n", "- Adjusting Threshold: If the threshold (red line) were adjusted (say to 0.3 or 0.7), the decision boundary (green line) would shift left or right, respectively, changing the prediction dynamics. Lowering the threshold would make the model more likely to predict class 1, while raising it would make the model more conservative, predicting class 0 more often.\n", "\n", "These two concepts allow logistic regression to make informed decisions based on the probability outputs, visualizing how data points are classified relative to both the input feature and the chosen threshold." ] }, { "cell_type": "markdown", "id": "2f88bebe-ac7f-4da7-b439-29c25140e2f0", "metadata": {}, "source": [ "## 2.9 Evaluating the Model with a Confusion Matrix\n", "\n", "Next, we will use a confusion matrix to evaluate the performance of our logistic regression model on the test set.\n", "\n", "\n", "#### (You)\n", "**Task:** 10 points\n", "\n", "1. Classify Based on the Best Threshold:\n", "\n", " - Use the threshold that produced the best results in the previous step to classify the predicted probabilities into binary outcomes (`0` or `1`).\n", "

\n", "\n", "\n", "2. Generate and Display a Confusion Matrix:\n", "\n", " - Calculate the confusion matrix to evaluate how well the logistic regression model performs on the test set using the chosen threshold.\n", " - Display the confusion matrix with appropriate color coding to visualize true positives, true negatives, false positives, and false negatives.\n", "

\n", "\n", "3. Add comments to your code:\n", " \n", " - Write comments above your code to ensure that anyone unfamiliar with your code can easily understand what you are doing. \n", "

\n", "\n", "4. Variables to Use:\n", " - predicted_classes_test\n", " - cm_test\n", " - custom_cmap\n", " - disp" ] }, { "attachments": { "image.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "88f028c8-7ed8-4b3c-a278-16e240b1729f", "metadata": {}, "source": [ "
\n", " Task Hint\n", "\n", "\n", "![image.png](attachment:image.png)\n", "\n", "A confusion matrix is a useful tool for analyzing how well a classification model performs, especially in binary classification tasks. It provides a detailed breakdown of the actual versus predicted classifications. Here's an in-depth explanation of each component of the confusion matrix shown in the image:\n", "\n", "**Structure of the Confusion Matrix**\n", "- Rows: Represent the actual classes (or true labels).\n", "- Columns: Represent the predicted classes (or predictions made by the model).\n", "\n", "**Components of the Confusion Matrix**\n", "1. True Positive (TP):\n", "\n", " - Location: Bottom-right cell.\n", " - Definition: The model correctly predicted the positive class (house with > 2 bathrooms).\n", " - Example: If 1 represents a house with > 2 bathrooms, and the model predicts 1 for an actual 1, it counts as a TP.\n", " - Interpretation: A high number of TPs indicates the model is good at identifying houses with > 2 bathrooms.\n", "

\n", "\n", "2. False Negative (FN):\n", "\n", " - Location: Bottom-left cell.\n", " - Definition: The model incorrectly predicted the negative class (house with <= 2 bathrooms) when the actual class was positive (house with > 2 bathrooms).\n", " - Example: If the actual class is 1 (a house with > 2 bathrooms), but the model predicts 0 (house with <= 2 bathrooms), this counts as an FN.\n", " - Interpretation: A high number of FNs suggests the model misses many houses with > 2 bathrooms, indicating lower sensitivity or recall.\n", "

\n", "\n", "3. False Positive (FP):\n", "\n", " - Location: Top-right cell.\n", " - Definition: The model incorrectly predicted the positive class (house with > 2 bathrooms) when the actual class was negative (house with <= 2 bathrooms).\n", " - Example: If 0 represents a house with <= 2 bathrooms, and the model predicts 1 (house with > 2 bathrooms), it counts as an FP.\n", " - Interpretation: A high number of FPs indicates the model often wrongly identifies houses with <= 2 bathrooms as having > 2 bathrooms, implying low precision.\n", "

\n", "\n", "4. True Negative (TN):\n", "\n", " - Location: Top-left cell.\n", " - Definition: The model correctly predicted the negative class (house with <= 2 bathrooms).\n", " - Example: If the actual class is 0 (a house with <= 2 bathrooms), and the model predicts 0, it counts as a TN.\n", " - Interpretation: A high number of TNs suggests the model is good at identifying houses with <= 2 bathrooms.\n", "\n", "```python\n", "'''\n", "Lines of code ≈ 15\n", "'''\n", "\n", "# Classify based on the threshold\n", "predicted_classes_test =\n", "\n", "# Calculate the confusion matrix\n", "cm_test =\n", "\n", "# Display the confusion matrix with color coding\n", "custom_cmap =\n", "disp =\n", "```" ] }, { "cell_type": "code", "execution_count": 23, "id": "6db75eb6-8af2-4d6b-9869-691be2ff3667", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-7c93895d6275f7e0", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### BEGIN SOLUTION\n", "# Classify based on the threshold\n", "predicted_classes_test = (y_hat_test >= threshold).astype(int)\n", "\n", "# Calculate the confusion matrix\n", "cm_test = confusion_matrix(y_test, predicted_classes_test)\n", "\n", "# Display the confusion matrix with color coding\n", "custom_cmap = LinearSegmentedColormap.from_list('custom_cmap', ['#4b4b4b','#ff8200'])\n", "disp = ConfusionMatrixDisplay(confusion_matrix=cm_test, display_labels=['<= 2 (0)', '> 2 (1)'])\n", "disp.plot(cmap=custom_cmap)\n", "plt.title('2.9: Confusion Matrix')\n", "plt.show()\n", "### END SOLUTION" ] }, { "attachments": { "416e55a2-9419-4dad-9e91-8de41f31881a.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "8e8d3a7c-e423-4c52-89e0-0c3e7e090591", "metadata": {}, "source": [ "
\n", " Expected Hint\n", "\n", "![image.png](attachment:416e55a2-9419-4dad-9e91-8de41f31881a.png)" ] }, { "cell_type": "markdown", "id": "801b43be-f484-46f2-8fd0-a427fdd2afb8", "metadata": {}, "source": [ "## 2.10 Generating a Classification Report\n", "\n", "Finally, we will generate a classification report to provide detailed performance metrics such as precision, recall, and F1-score.\n", "\n", "#### (You)\n", "**Task:** \n", "5 points\n", "\n", "1. Generate a Classification Report:\n", "\n", " - Use the `classification_report` function to generate a detailed report showing the precision, recall, and F1-score for each class (`<= 2 Bathrooms` and `> 2 Bathrooms`).\n", " - Store the generated classification report into a variable named `report`.\n", "\n", "

\n", "\n", "2. Interpret the Results:\n", "\n", " - Review the output to understand how well the logistic regression model performed in terms of `precision` (accuracy of positive predictions), `recall` (ability to find all positive instances), and `F1-score` (harmonic mean of precision and recall).\n", " \n", "

\n", "\n", "3. Add comments to your code:\n", " \n", " - Write comments above your code to ensure that anyone unfamiliar with your code can easily understand what you are doing.\n", " \n", "4. Variables to Use:\n", " - report" ] }, { "cell_type": "markdown", "id": "40a5b8dc-ad37-4669-af12-98c8094aa23f", "metadata": {}, "source": [ "
\n", " Task Hint\n", "\n", "\n", "- A classification report provides insights into how well a model performs across different classes, which is important for understanding model effectiveness.\n", "\n", "```python\n", "'''\n", "Lines of code ≈ 2\n", "'''\n", "\n", "# Generate the classification report\n", "report =\n", "\n", "# Display classification report for the test set\n", "\n", "```" ] }, { "cell_type": "code", "execution_count": 17, "id": "4d1f1225-2657-4b90-a092-28ab74b176f5", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-db27cbe6b55e280a", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Classification Report:\n", " precision recall f1-score support\n", "\n", "<= 2 Bathrooms 0.89 0.90 0.90 256\n", " > 2 Bathrooms 0.89 0.88 0.88 235\n", "\n", " accuracy 0.89 491\n", " macro avg 0.89 0.89 0.89 491\n", " weighted avg 0.89 0.89 0.89 491\n", "\n" ] } ], "source": [ "### BEGIN SOLUTION\n", "# Generate the classification report\n", "report = classification_report(y_test, predicted_classes_test, target_names=['<= 2 Bathrooms', '> 2 Bathrooms'])\n", "\n", "# Display classification report for the test set\n", "print(f\"Classification Report:\\n{report}\")\n", "### END SOLUTION" ] }, { "attachments": { "b8c3e427-8efb-49f6-930a-22bf1af044fa.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "9a293a96-7cf9-459b-8e35-6e3ea5c5925d", "metadata": {}, "source": [ "
\n", " Expected Hint\n", "\n", "![image.png](attachment:b8c3e427-8efb-49f6-930a-22bf1af044fa.png)\n", "\n", "To understand the classification report better, let's go through the calculations for each metric: precision, recall, F1-score, and accuracy.\n", "\n", "### **Formulas**\n", "\n", "**Metrics Calculation Formulas**\n", "\n", "1. Precision:\n", " \n", "$$Precision= \\frac{TP}{TP+FP}$$\n", "\n", "2. Recall (Sensitivity):\n", "\n", "$$Recall=\\frac{TP}{TP+FN}$$ \n", "\n", "3. F1-Score:\n", "\n", "$$F1 = 2× \\frac{Precision×Recall}{Precision+Recall}$$\n", "\n", " \n", "4. Accuracy:\n", "\n", "$$Accuracy= \\frac{TP+TN}{TP+TN+FP+FN}$$\n", "\n", " \n", "Support: Number of actual occurrences of the class in the dataset.\n", "\n", "### **Calculations for Each Class**\n", "\n", "#### **Class: <= 2 Bathrooms (1)**\n", "\n", "**Confusion Matrix Values**\n", "\n", "- True Positive (TP): 231\n", "- False Negative (FN): 25\n", "- False Positive (FP): 31\n", "- True Negative (TN): 204\n", "\n", "**Calculating the Metrics**\n", "\n", "1. Precision:\n", " \n", "$$Precision= \\frac{TP}{TP+FP} = \\frac{231}{231+31} = \\frac{231}{262} ≈ 0.88$$\n", "\n", "2. Recall:\n", "\n", "$$Recall= \\frac{TP}{TP+FN} = \\frac{231}{231+25} = \\frac{231}{256} ≈ 0.90$$\n", "\n", "3. F1-Score:\n", "\n", "$$F1-Score=2× \\frac{Precision×Recall}{Precision+Recall} =2×\\frac{0.88×0.90}{0.88+0.90} ≈ 0.89$$\n", "\n", "4. Accuracy:\n", "\n", "$$Accuracy= \\frac{TP+TN}{TP+TN+FP+FN} = \\frac{231+204}{231+204+31+25} = \\frac{435}{491} ≈ 0.89$$\n", "\n", "\n", "**Summary**\n", "\n", "1. Precision: 0.88\n", "\n", "2. Recall: 0.90\n", "\n", "3. F1-Score: 0.89\n", "\n", "4. Accuracy: 0.89\n", "\n", "\n", "

\n", "\n", "\n", "#### **Class: > 2 Bathrooms (0)**\n", "\n", "**Confusion Matrix Values**\n", "\n", "- True Positive (TP): 204 (same as TN for the other class)\n", "- False Negative (FN): 31 (same as FP for the other class)\n", "- False Positive (FP): 25 (same as FN for the other class)\n", "- True Negative (TN): 231 (same as TP for the other class)\n", "\n", "**Calculating the Metrics**\n", "\n", "1. Precision:\n", " \n", "$$Precision= \\frac{TP}{TP+FP} = \\frac{204}{204+25} = \\frac{204}{229} ≈ 0.89$$\n", "\n", "2. Recall:\n", "\n", "$$Recall= \\frac{TP}{TP+FN} = \\frac{204}{204+31} = \\frac{204}{256} ≈ 0.87$$\n", "\n", "3. F1-Score:\n", "\n", "$$F1-Score=2× \\frac{Precision×Recall}{Precision+Recall} =2×\\frac{0.89×0.87}{0.89+0.87} ≈ 0.88$$\n", "\n", "4. Accuracy:\n", "\n", "$$Accuracy= \\frac{TP+TN}{TP+TN+FP+FN} = \\frac{231+204}{231+204+31+25} = \\frac{435}{491} ≈ 0.89$$\n", "\n", "\n", "**Summary**\n", "\n", "1. Precision: 0.89\n", " \n", "2. Recall: 0.87\n", "\n", "3. F1-Score: 0.88\n", "\n", "4. Accuracy: 0.89" ] }, { "cell_type": "markdown", "id": "b89785ef-e1bf-4557-a705-517a837a4483", "metadata": {}, "source": [ "\n", "# Part 3: Knowledge Check\n", "\n", "#### Worth: 20%\n", "\n", "Given the house sizes and number of bathrooms listed below, determine whether the Logistic Regression model you trained would likely predict those values correctly. Assign each house to one of the following categories based on your analysis: True Positive (TP), False Negative (FN), False Positive (FP), or True Negative (TN).\n", "\n", "After deciding, set the corresponding variable (A, B, C, D) to TP, FN, FP, or TN to reflect your conclusion for each scenario.\n", "\n", "\n", "| **Problem** | **House Size** | **Bathrooms** |\n", "|-------------|----------------|---------------|\n", "| **A.** | 1700 | 4 | \n", "| **B.** | 1900 | 2 |\n", "| **C.** | 2100 | 3 |\n", "| **D.** | 2000 | 2 |\n", "\n", "\n", "**Instructions:**\n", "\n", "For each house, set the corresponding variable (A, B, C, D) to either TP, FN, FP, or TN based on your determination.\n", "\n", "Example:\n", "\n", "E = 'TP'\n", "\n", "```\n", "A = ''\n", "\n", "B = ''\n", "\n", "C = ''\n", "\n", "D = ''\n", "```" ] }, { "cell_type": "code", "execution_count": 24, "id": "2039d5e9-8d39-4b68-a292-74bd87e71c0a", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-58e5f245845d197f", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A = FN\n", "B = FP\n", "C = TP\n", "D = FP\n" ] } ], "source": [ "### BEGIN SOLUTION\n", "# Helper function to predict the class based on house size\n", "def predict_bathroom_class(house_size, theta_optimized, threshold):\n", " z = theta_optimized[0] + theta_optimized[1] * house_size\n", " probability = sigmoid(z)\n", " return int(probability >= threshold)\n", "\n", "# House data from the table\n", "houses = {\n", " 'A': {'house_size': 1700, 'bath': 4},\n", " 'B': {'house_size': 1900, 'bath': 2},\n", " 'C': {'house_size': 2100, 'bath': 3},\n", " 'D': {'house_size': 2000, 'bath': 2},\n", "}\n", "\n", "# Determine the classification for each\n", "results = {}\n", "for key, value in houses.items():\n", " house_size = value['house_size']\n", " actual_bath_class = int(value['bath'] > 2) # Class based on actual bathrooms\n", " predicted_class = predict_bathroom_class(house_size, theta_optimized, threshold)\n", " \n", " if predicted_class == 1 and actual_bath_class == 1:\n", " results[key] = 'TP'\n", " elif predicted_class == 0 and actual_bath_class == 0:\n", " results[key] = 'TN'\n", " elif predicted_class == 1 and actual_bath_class == 0:\n", " results[key] = 'FP'\n", " elif predicted_class == 0 and actual_bath_class == 1:\n", " results[key] = 'FN'\n", "\n", "# Assign results to the variables\n", "A = results['A']\n", "B = results['B']\n", "C = results['C']\n", "D = results['D']\n", "\n", "# Print the results\n", "print(f\"A = {A}\")\n", "print(f\"B = {B}\")\n", "print(f\"C = {C}\")\n", "print(f\"D = {D}\")\n", "### END SOLUTION" ] }, { "cell_type": "code", "execution_count": null, "id": "d64605ae-a98b-44a1-9507-aaa4c16ff882", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.7" } }, "nbformat": 4, "nbformat_minor": 5 }