Main Content

Generalized linear regression models with various distributions and link functions, including logistic regression

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.