## Python binding reference

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