Documentation

Regularization

Ridge regression, lasso, elastic nets

For greater accuracy and link-function choices on low- through medium-dimensional data sets, fit a generalized linear model with a lasso penalty using lassoglm.

For reduced computation time on high-dimensional data sets that fit in the MATLAB® Workspace, train a binary, linear classification model, such as a regularized logistic regression model, using fitclinear. You can also efficiently train a multiclass error-correcting output codes (ECOC) model composed of logistic regression models using fitcecoc.

Classes

ClassificationLinear Linear model for binary classification of high-dimensional data
ClassificationECOC Multiclass model for support vector machines or other classifiers
ClassificationPartitionedLinear Cross-validated linear model for binary classification of high-dimensional data
ClassificationPartitionedLinearECOC Cross-validated linear error-correcting output codes model for multiclass classification of high-dimensional data

Functions

lassoglm Lasso or elastic net regularization for generalized linear model regression
fitclinear Fit linear classification model to high-dimensional data
templateLinear Linear classification learner template
fitcecoc Fit multiclass models for support vector machines or other classifiers
predict Predict labels for linear classification models
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