mlpack 4.6.1

a flexible, fast machine learning library

mlpack is a fast, flexible machine learning library, written in C++, that aims to provide fast, extensible implementations of cutting-edge machine learning algorithms. mlpack provides these algorithms as standalone Python functions, which wrap the fast C++ implementations of the algorithms.

mlpack's techniques fall into a handful of categories:

• Classification: logistic regression, perceptrons, random forests, linear SVMs, AdaBoost, etc.

• Regression: linear regression, least angle regression, etc.

• Clustering: Gaussian mixture models, k-means, mean shift, DBSCAN, etc.

• Geometry: k-nearest-neighbor search, max-kernel search, locality sensitive hashing (LSH), etc.

• Preprocessing: dataset splitting, binarization, scaling, one hot encoding, etc.

• Misc. / Other: collaborative filtering, density estimation trees, Hidden Markov Models, kernel density estimation (KDE), etc.

• Transformations: kernel PCA, sparse coding, large margin nearest neighbors, PCA, etc.

For more documentation on each individual function that mlpack provides, see the Python binding documentation.