|Linear model for binary classification of high-dimensional data|
|Multiclass model for support vector machines (SVMs) and other classifiers|
|Gaussian kernel classification model using random feature expansion|
|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|
|Add terms to generalized linear regression model|
|Remove terms from generalized linear regression model|
|Improve generalized linear regression model by adding or removing terms|
|Predict responses of generalized linear regression model using one input for each predictor|
|Predict responses of generalized linear regression model|
|Simulate responses with random noise for generalized linear regression model|
|Confidence intervals of coefficient estimates of generalized linear regression model|
|Linear hypothesis test on generalized linear regression model coefficients|
|Analysis of deviance for generalized linear regression model|
|Plot observation diagnostics of generalized linear regression model|
|Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots|
|Plot residuals of generalized linear regression model|
|Plot of slices through fitted generalized linear regression surface|
|Fit linear classification model to high-dimensional data|
|Fit multiclass models for support vector machines or other classifiers|
|Fit Gaussian kernel classification model using random feature expansion|
|Linear classification learner template|
Generalized linear models use linear methods to describe a potentially nonlinear relationship between predictor terms and a response variable.
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.
Wilkinson notation provides a way to describe regression and repeated measures models without specifying coefficient values.
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.