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
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
For nonlinear classification with big data, train a binary, Gaussian kernel classification model with logistic regression by using
|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|
|Compute partial dependence|
|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|
|Gather properties of machine learning model from GPU|
|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.
Fit and evaluate generalized linear models using
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.