pyFM

Implements stochastic gradient descent with adaptive regularization that automatically adjusts during training, preventing overfitting without manual hyperparameter tuning, as evidenced by the training logs showing decreasing MSE.

Designed to work with scikit-learn's DictVectorizer for easy feature encoding from dictionary data, simplifying preprocessing and fitting into existing Python machine learning workflows.

Supports both regression and classification tasks with configurable parameters, demonstrated in the README examples for rating prediction and binary classification.

Accepts categorical and real-valued features transformed into sparse matrices via DictVectorizer, mimicking libFM's approach for high-dimensional sparse data common in recommendation systems.