Regression models for limited responses

For greater accuracy and link-function choices on low- through
medium-dimensional data sets, fit a generalized linear model using `fitglm`

.

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 `fitclinear`

.
You can also efficiently train a multiclass error-correcting output
codes (ECOC) model composed of logistic regression models using `fitcecoc`

.

`GeneralizedLinearModel` |
Generalized linear regression model class |

`ClassificationLinear` |
Linear model for binary classification of high-dimensional data |

`ClassificationECOC` |
Multiclass model for support vector machines or other classifiers |

`ClassificationPartitionedLinear` |
Cross-validated linear model for binary classification of high-dimensional data |

`ClassificationPartitionedLinearECOC` |
Cross-validated linear error-correcting output codes model for multiclass classification of high-dimensional data |

`fitglm` |
Create generalized linear regression model |

`stepwiseglm` |
Create generalized linear regression model by stepwise regression |

`disp` |
Display generalized linear regression model |

`feval` |
Evaluate generalized linear regression model prediction |

`predict` |
Predict response of generalized linear regression model |

`random` |
Simulate responses for generalized linear regression model |

`fitclinear` |
Fit linear classification model to high-dimensional data |

`templateLinear` |
Linear classification learner template |

`fitcecoc` |
Fit multiclass models for support vector machines or other classifiers |

`predict` |
Predict labels for linear classification models |

**Generalized Linear Model Workflow**

Fit a generalized linear model and analyze the results.

**Train Logistic Regression Classifiers Using Classification
Learner App**

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.

**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.

Wilkinson notation provides a way to describe regression and repeated measures models without specifying coefficient values.

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