Some interesting new algorithms
Just wanted to note down some new algorithms that I came across for future reference. Haven’t actually tried any of these yet.
- LIBFMM, a library for Field-aware Factorization machines. Developed by a group at National Taiwan University, this technique has been used to win two Kaggle click-through competitions. (Criteo, Avazu)
- Random Bits Regression, a “strong general predictor for big data” (paper). “This method first generates a large number of random binary intermediate/derived features based on the original input matrix, and then performs regularized linear/logistic regression on those intermediate/derived features to predict the outcome.“
- BIDMach, a CPU and GPU-accelerated machine learning library that shows some amazing benchmark results compared to Spark, Vowpal Wabbit, scikit-learn etc.
And another one which is not as new, but which I wanted to highlight because of a nice blog post about interactions and generalization by David Chudzicki:
- BART, Bayesian additive regressive trees (PDF filnk). David’s blog post: An Interaction or Not? How a few ML Models Generalize to New Data