Details

Gradient Boosting is a powerful machine learning method used for both regression and classification tasks. It builds a strong predictive model by combining multiple weak learners, where each subsequent model is trained to reduce the loss function—such as mean squared error or cross-entropy—of the previous model using gradient descent. This project demonstrates a Gradient Boosting Regressor built entirely from scratch using only Python and NumPy. It is designed to provide a hands-on understanding of Gradient Boosting without relying on external machine learning libraries like scikit-learn, making it both a practical implementation and a valuable learning resource.