Logistic Regression (LR) Classifier in Machine Learning with MATLAB Implementation

This document explores the Logistic Regression (LR) method in machine learning, a fundamental classifier for binary classification problems. We provide complete MATLAB source code implementation accompanied by four distinct datasets for experimental validation. Logistic regression serves as a widely-adopted machine learning algorithm that predicts categorical outcomes based on input features through sigmoid function transformation. The implementation includes key components: data preprocessing, hypothesis function formulation using sigmoid activation, cost function calculation with logarithmic loss, and gradient descent optimization for parameter updates. The MATLAB code demonstrates practical implementation of maximum likelihood estimation and decision boundary visualization. Four curated datasets cover various scenarios to test model performance, including feature scaling implementation and regularization techniques to prevent overfitting. Through hands-on experimentation with the provided code and datasets, users can gain deeper insights into LR's working mechanism, including probability calibration, classification threshold tuning, and performance evaluation metrics like precision-recall analysis. This resource enables practical application of logistic regression to diverse domains including medical diagnosis, financial risk assessment, and marketing analytics.