mlpack provides Python bindings for many of the algorithms it implements. These may be used to perform many machine learning tasks without the overhead of writing C++, or may be used as part of a larger machine learning solution.

Below is a list of the Python bindings mlpack (git master version) provides, with links to the documentation for each executable. This documentation may also be accessed by using Python's help() method.

• adaboost(): train and classify with AdaBoost, an ensembling classifier
• cf(): generate recommendations via collaborative filtering
• dbscan(): perform DBSCAN clustering
• decision_stump(): classify with a decision stump
• decision_tree(): classify with a decision tree
• det(): density estimation trees
• emst(): calculate a Euclidean minimum spanning tree
• fastmks(): perform fast max-kernel search with trees
• gmm_train(): train a Gaussian mixture model
• gmm_generate(): generate a random sequence from a GMM
• gmm_probability(): calculate the probability of a set of points coming from a given GMM
• hmm_generate(): generate observations from a hidden Markov model (HMM)
• hmm_loglik(): calculate the log-likelihood of some observations from an HMM
• hmm_train(): train a hidden Markov model (HMM)
• hmm_viterbi(): find the most probable hidden states in an HMM for some observations
• hoeffding_tree(): train and classify with Hoeffding trees, a streaming decision tree for very large datasets
• kernel_pca(): perform kernel principal components analysis
• kmeans(): perform k-means clustering
• lars(): least-angle regression
• linear_regression(): simple least-squares linear regression
• local_coordinate_coding(): local coordinate coding
• logistic_regression(): train or classify with logistic regression
• lsh(): approximate k-nearest neighbor search with locality-sensitive hashing
• mean_shift(): mean shift clustering
• nbc(): train or classify with the naive Bayes classifier
• nca(): neighborhood components analysis
• nmf(): non-negative matrix factorization
• pca(): principal components analysis
• perceptron(): train or classify with a perceptron
• radical(): RADICAL (independent components analysis)
• softmax_regression(): train or classify with softmax regression
• sparse_coding(): sparse coding with dictionary learning