For greater accuracy and link-function choices on low- through
medium-dimensional data sets, fit a generalized linear model using
For reduced computation time on high-dimensional data sets that
fit in the MATLAB® Workspace, train a binary, linear classification
model, such as a logistic regression model, using
You can also efficiently train a multiclass error-correcting output
codes (ECOC) model composed of logistic regression models using
||Generalized linear regression model class|
||Linear model for binary classification of high-dimensional data|
||Multiclass model for support vector machines or other classifiers|
||Cross-validated linear model for binary classification of high-dimensional data|
||Cross-validated linear error-correcting output codes model for multiclass classification of high-dimensional data|
||Create generalized linear regression model|
||Create generalized linear regression model by stepwise regression|
||Display generalized linear regression model|
||Evaluate generalized linear regression model prediction|
||Predict response of generalized linear regression model|
||Simulate responses for generalized linear regression model|
||Fit linear classification model to high-dimensional data|
||Linear classification learner template|
||Fit multiclass models for support vector machines or other classifiers|
||Predict labels for linear classification models|
Fit a generalized linear model and analyze the results.
Learn how to train logistic regression classifiers.
Generalized linear models use linear methods to describe a potentially nonlinear relationship between predictor terms and a response variable.
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.
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.
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.
Wilkinson notation provides a way to describe regression and repeated measures models without specifying coefficient values.