MATLAB Implementation of Logistic Regression Algorithm

I have developed a custom logistic regression program in MATLAB that demonstrates both simplicity in understanding and exceptional computational efficiency. This implementation utilizes vectorized operations and optimized matrix calculations to handle large datasets effectively. The core algorithm employs sigmoid function transformation with gradient descent optimization, allowing for flexible data analysis configurations based on specific requirements. Key features include: - Batch gradient descent implementation with customizable learning rates - Regularization options (L1/L2) to prevent overfitting - Automatic feature normalization and data preprocessing - Convergence monitoring with early stopping criteria The program's modular architecture enables straightforward customization for extended functionality, such as multi-class classification through one-vs-all methodology or alternative optimization algorithms like Newton-Raphson. Through this tool, users can gain deeper insights into their data patterns and extract meaningful information efficiently. This implementation serves as a robust foundation for binary classification tasks, with well-documented code structure that facilitates modification and adaptation to various data science applications. The algorithm's efficiency makes it particularly suitable for iterative model tuning and large-scale data processing scenarios.