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Regresión lineal generalizada

Modelos de regresión para respuestas limitadas

Para una mayor precisión y las opciones de función de enlace en los conjuntos de datos de bajo a medio dimensional, ajuste un modelo lineal generalizado utilizando fitglm.

Para un tiempo de cómputo reducido en conjuntos de datos de alta dimensión que caben en el espacio de trabajo MATLAB®, capacite un modelo de clasificación lineal binario, como un modelo de regresión logística, utilizando fitclinear. También puede entrenar eficientemente un modelo de códigos de salida de corrección de errores multiclase (ECOC) compuesto por modelos de regresión logística utilizando fitcecoc.

Para la clasificación no lineal con datos grandes, entrenar un binario, modelo de clasificación de núcleo de Gauss con regresión logística usando fitckernel.

Clases

GeneralizedLinearModelGeneralized linear regression model class
CompactGeneralizedLinearModelCompact generalized linear regression model class
ClassificationLinearLinear model for binary classification of high-dimensional data
ClassificationECOCMulticlass model for support vector machines or other classifiers
ClassificationKernelGaussian kernel classification model using random feature expansion
ClassificationPartitionedLinearCross-validated linear model for binary classification of high-dimensional data
ClassificationPartitionedLinearECOCCross-validated linear error-correcting output codes model for multiclass classification of high-dimensional data

Funciones

fitglmCreate generalized linear regression model
stepwiseglmCreate generalized linear regression model by stepwise regression
compactCompact generalized linear regression model
dispDisplay generalized linear regression model
fevalEvaluate generalized linear regression model prediction
predictPredict response of generalized linear regression model
randomSimulate responses for generalized linear regression model
fitclinearFit linear classification model to high-dimensional data
templateLinearLinear classification learner template
fitcecocFit multiclass models for support vector machines or other classifiers
predictPredict labels for linear classification models
fitckernelFit Gaussian kernel classification model using random feature expansion
predictPredict labels for Gaussian kernel classification model
mnrfitMultinomial logistic regression
mnrvalMultinomial logistic regression values
glmfitGeneralized linear model regression
glmvalGeneralized linear model values
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots

Ejemplos y procedimientos

Generalized Linear Model Workflow

Fit a generalized linear model and analyze the results.

Train Logistic Regression Classifiers Using Classification Learner App

Create and compare logistic regression classifiers, and export trained models to make predictions for new data.

Conceptos

Generalized Linear Models

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

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