An interactive tool for visualizing logistic regression, gradient descent, and regularization in action. The computational core (loss calculation, gradients, and weight updates) is written in C++ and ...
An XGBoost-like gradient boosting implementation using only Python and NumPy. Features Newton boosting (second-order optimization), histogram-based split finding, L1/L2 regularization, and custom loss ...
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