Modeling

mlpack contains numerous different machine learning algorithms that can be used for modeling.

Note: this section is under construction and not all functionality is documented yet.

🔗 Classification

Classify points as discrete labels (0, 1, 2, …).

AdaBoost: Adaptive Boosting
DecisionTree: ID3-style decision tree classifier
HoeffdingTree: streaming/incremental decision tree classifier
LinearSVM: simple linear support vector machine classifier
LogisticRegression: L2-regularized logistic regression (two-class only)
NaiveBayesClassifier: simple multi-class naive Bayes classifier
Perceptron: simple Perceptron classifier
RandomForest: parallelized random forest classifier
SoftmaxRegression: L2-regularized softmax regression (i.e. multi-class logistic regression)

🔗 Regression

Predict continuous values.

BayesianLinearRegression: Bayesian L2-penalized linear regression
DecisionTreeRegressor: ID3-style decision tree regressor
LARS: Least Angle Regression (LARS), L1-regularized and L2-regularized
LinearRegression: L2-regularized linear regression (ridge regression)

🔗 Clustering

NOTE: this documentation is still under construction and so some algorithms that mlpack implements are not yet listed here. For now, see the mlpack/methods directory for a full list of algorithms.

Group points into clusters.

MeanShift: clustering with the density-based mean shift algorithm

🔗 Geometric algorithms

NOTE: this documentation is still under construction and so no geometric algorithms in mlpack are documented yet. For now, see the mlpack/methods directory for a full list of algorithms.

Computations based on distance metrics.