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
For nonlinear classification with big data, train a binary, Gaussian
kernel classification model with logistic regression using
|Generalized linear regression model class|
|Compact generalized linear regression model class|
|Linear model for binary classification of high-dimensional data|
|Multiclass model for support vector machines or other classifiers|
|Gaussian kernel classification model using feature expansion for big data|
|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|
|Compact generalized linear regression model|
|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 Gaussian kernel classification model using feature expansion for big data|
|Predict labels for Gaussian kernel classification model|
|Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots|
Fit a generalized linear model and analyze the results.
Create and compare logistic regression classifiers, and export trained models to make predictions for new data.
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.