Documentation

This is machine translation

Translated by Microsoft
Mouse over text to see original. Click the button below to return to the English verison of the page.

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

Examples and How To

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.

Concepts

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.

Was this topic helpful?