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.
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 |
lassoglm | Lasso or elastic net regularization for generalized linear models |
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 |
fitckernel | Fit Gaussian kernel classification model using random feature expansion |
predict | Predict labels for Gaussian kernel classification model |
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.