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Ridge regression, lasso, and elastic nets for generalized linear models

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, 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.

For nonlinear classification with big data, train a binary, Gaussian kernel classification model with regularized logistic regression using fitckernel.


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lassoglmLasso or elastic net regularization for generalized linear models
fitclinearFit binary linear classifier to high-dimensional data
templateLinearLinear learner template
fitcecocFit multiclass models for support vector machines or other classifiers
predictPredict labels for linear classification models
fitckernelFit binary Gaussian kernel classifier using random feature expansion
predictPredict labels for Gaussian kernel classification model


ClassificationLinearLinear model for binary classification of high-dimensional data
ClassificationECOCMulticlass model for support vector machines (SVMs) and other classifiers
ClassificationKernelGaussian kernel classification model using random feature expansion
ClassificationPartitionedLinearCross-validated linear model for binary classification of high-dimensional data
ClassificationPartitionedLinearECOCCross-validated linear error-correcting output codes model for multiclass classification of high-dimensional data