MATLAB Implementation of Lasso Algorithm with Stanford Source Code

The Lasso algorithm is a widely-used machine learning method originally developed by Stanford University, proven to be highly reliable in practical applications. This algorithm primarily serves for feature selection and regression analysis, with its core concept focusing on data sparsification to identify features that significantly impact target variables. The MATLAB implementation typically utilizes coordinate descent optimization to solve the L1-regularized regression problem, efficiently handling high-dimensional datasets through iterative coefficient updates and thresholding operations. In practical implementations, the code includes key functions for parameter tuning via cross-validation, where the optimal lambda value is determined by minimizing prediction error across different regularization strengths. The algorithm's feature selection capability automatically drives irrelevant coefficients to zero, creating sparse models that enhance interpretability and prevent overfitting. Across various domains including finance, healthcare, and image processing, Lasso algorithm has demonstrated remarkable results in predictive modeling and dimensionality reduction. The MATLAB source code provides comprehensive functionality for data preprocessing, model training, and result visualization, making it highly recommended for data analysis and prediction tasks. The implementation includes error handling for numerical stability and supports both dense and sparse matrix operations for computational efficiency.