{
"cells": [
{
"cell_type": "markdown",
"id": "c49480db",
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"source": [
"(chapter11_part1)=\n",
"\n",
"# Maximum Margin Models\n",
"\n",
"- This is a supplement material for the [Machine Learning Simplified](https://themlsbook.com) book. It sheds light on Python implementations of the topics discussed while all detailed explanations can be found in the book. \n",
"- I also assume you know Python syntax and how it works. If you don't, I highly recommend you to take a break and get introduced to the language before going forward with my code. \n",
"- This material can be downloaded as a Jupyter notebook (Download button in the upper-right corner -> `.ipynb`) to reproduce the code and play around with it. \n",
"\n",
"\n",
"This notebook is a supplement for *Chapter 11. Maximum Margin Models* of **Machine Learning For Everyone** book.\n",
"\n",
"## 1. Required Libraries\n",
"\n",
"Let's import required libraries:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "a24d90df",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn.datasets import make_classification\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.svm import SVC\n",
"from sklearn.metrics import accuracy_score, confusion_matrix\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"id": "64dcfaaf",
"metadata": {},
"source": [
"## 2. Create a Synthetic Dataset\n",
"\n",
"To demonstrate the application of a Maximum Margin Model using both Linear SVM (Support Vector Machine) and Kernelized SVM, let's first create a synthetic dataset in Python. We will use this dataset to train and evaluate our models.\n",
"\n",
"We'll use make_classification from scikit-learn to generate a binary classification dataset."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "2f30c9b4",
"metadata": {},
"outputs": [],
"source": [
"# Step 1: Create a synthetic dataset\n",
"X, y = make_classification(n_samples=200, n_features=2, n_redundant=0, n_informative=2,\n",
" random_state=1, n_clusters_per_class=1, flip_y=0.1)\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "f7c4247c",
"metadata": {},
"outputs": [],
"source": [
"# Splitting the dataset into training and testing sets\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)"
]
},
{
"cell_type": "markdown",
"id": "400aabc1",
"metadata": {},
"source": [
"## 2. Implement Linear SVM and Kernelized SVM\n",
"\n",
"We will use the SVC (Support Vector Classifier) from scikit-learn, applying both linear and kernelized (e.g., RBF) approaches."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f6d597ff",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"SVC(kernel='linear')
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