For greater accuracy and link function choices on low-dimensional through medium-dimensional data sets, fit a generalized linear regression model using fitglm. For a multinomial logistic regression, fit a model using mnrfit.
To reduce computation time on high-dimensional data sets, train a binary, linear classification model, such as a logistic regression model, by using fitclinear. You can also efficiently train a multiclass error-correcting output codes (ECOC) model composed of logistic regression models by using fitcecoc.
For nonlinear classification with big data, train a binary, Gaussian kernel classification model with logistic regression by using fitckernel.
Generalized linear models use linear methods to describe a potentially nonlinear relationship between predictor terms and a response variable.
Generalized Linear Model Workflow
Fit a generalized linear model and analyze the results.
Fitting Data with Generalized Linear Models
Fit and evaluate generalized linear models using glmfit
and glmval.
Train Logistic Regression Classifiers Using Classification Learner App
Create and compare logistic regression classifiers, and export trained models to make predictions for new data.
Wilkinson notation provides a way to describe regression and repeated measures models without specifying coefficient values.
Multinomial Models for Nominal Responses
A nominal response variable has a restricted set of possible values with no natural order between them. A nominal response model explains and predicts the probability that an observation is in each category of a categorical response variable.
Multinomial Models for Ordinal Responses
An ordinal response variable has a restricted set of possible values that fall into a natural order. An ordinal response model describes the relationship between the cumulative probabilities of the categories and predictor variables.
Hierarchical Multinomial Models
A hierarchical multinomial response variable (also known as a sequential or nested multinomial response) has a restricted set of possible values that fall into hierarchical categories. The hierarchical multinomial regression models are extensions of binary regression models based on conditional binary observations.