Regularization

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.

Functions

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Regularization Without Using Object

lassoglm Lasso or elastic net regularization for generalized linear models

Regularized Logistic Regression Using Linear, ECOC, or Kernel Model

`fitclinear`	Fit binary linear classifier to high-dimensional data
`templateLinear`	Linear learner template
`fitcecoc`	Fit multiclass models for support vector machines or other classifiers
`predict`	Predict labels for linear classification models
`fitckernel`	Fit binary Gaussian kernel classifier using random feature expansion
`predict`	Predict labels for Gaussian kernel classification model

Objects

`ClassificationLinear`	Linear model for binary classification of high-dimensional data
`ClassificationECOC`	Multiclass model for support vector machines (SVMs) and other classifiers
`ClassificationKernel`	Gaussian kernel classification model using random feature expansion
`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

Topics

Regularize Poisson Regression
Identify and remove redundant predictors from a generalized linear model.
Regularize Logistic Regression
Regularize binomial regression.
Regularize Wide Data in Parallel
Regularize a model with many more predictors than observations.
Lasso Regularization of Generalized Linear Models
The lasso algorithm produces a smaller model with fewer predictors. The related elastic net algorithm can be more accurate when predictors are highly correlated.