## command-line program reference

mlpack provides command-line executables 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 command-line executables mlpack 3.0.0 provides, with links to the documentation for each executable. This documentation may also be accessed with the --help parameter or through the man pages provided with your distribution of mlpack.

- mlpack_adaboost: train and classify with AdaBoost, an ensembling classifier
- mlpack_approx_kfn:
approximate
*k*-furthest neighbor search - mlpack_cf: generate recommendations via collaborative filtering
- mlpack_dbscan: DBSCAN clustering
- mlpack_decision_stump: classify with a decision stump
- mlpack_decision_tree: classify with a decision tree
- mlpack_det: density estimation trees
- mlpack_emst: calculate a Euclidean minimum spanning tree
- mlpack_fastmks: perform fast max-kernel search with trees
- mlpack_gmm_train: train a Gaussian mixture model
- mlpack_gmm_generate: generate a random sequence from a GMM
- mlpack_gmm_probability: calculate the probability of a set of points coming from a given GMM
- mlpack_hmm_generate: generate observations from a hidden Markov model (HMM)
- mlpack_hmm_loglik: calculate the log-likelihood of some observations from an HMM
- mlpack_hmm_train: train a hidden Markov model (HMM)
- mlpack_hmm_viterbi: find the most probable hidden states in an HMM for some observations
- mlpack_hoeffding_tree: train and classify with Hoeffding trees, a streaming decision tree for very large datasets
- mlpack_kernel_pca: perform kernel principal components analysis
- mlpack_kfn: all
*k*-furthest neighbor search with trees - mlpack_kmeans: perform
*k*-means clustering - mlpack_knn: all
*k*-nearest neighbor search with trees - mlpack_krann: rank-approximate
*k*-nearest neighbor search with trees - mlpack_lars: least-angle regression
- mlpack_linear_regression: simple least-squares linear regression
- mlpack_local_coordinate_coding: local coordinate coding
- mlpack_logistic_regression: train or classify with logistic regression
- mlpack_lsh: approximate
*k*-nearest neighbor search with locality-sensitive hashing - mlpack_mean_shift: mean shift clustering
- mlpack_nbc: train or classify with the naive Bayes classifier
- mlpack_nca: neighborhood components analysis
- mlpack_nmf: non-negative matrix factorization
- mlpack_pca: principal components analysis
- mlpack_perceptron: train or classify with a perceptron
- mlpack_preprocess_binarize: binarize features of a dataset
- mlpack_preprocess_imputer: impute missing values of a dataset
- mlpack_preprocess_describe: generative descriptive statistics for a dataset
- mlpack_preprocess_split: split a dataset into a training and test set
- mlpack_radical: RADICAL (independent components analysis)
- mlpack_random_forest: random forest classifier
- mlpack_range_search: range search with trees
- mlpack_softmax_regression: train or classify with softmax regression
- mlpack_sparse_coding: sparse coding with dictionary learning