Generalisierte lineare Regression

Generalisierte lineare Regressionsmodelle mit verschiedenen Verteilungen und Link-Funktionen, einschließlich logistischer Regression

Um eine höhere Präzision und mehr Auswahlmöglichkeiten für die Link-Funktion bei niederdimensionalen bis mitteldimensionalen Datensätzen zu erreichen, können Sie ein generalisiertes lineares Regressionsmodell mithilfe von fitglm anpassen. Für eine multinomiale logistische Regression passen Sie ein Modell mithilfe von fitmnr an.

Um die Berechnungszeit bei höherdimensionalen Datensätzen zu reduzieren, trainieren Sie ein binäres, lineares Klassifizierungsmodell wie ein logistisches Regressionsmodell mithilfe von fitclinear. Ebenfalls können Sie ein mehrklassiges ECOC-Modell (Error-Correcting Output Codes, fehlerkorrigierende Ausgangscodes) aus logistischen Regressionsmodellen mithilfe von fitcecoc effizient trainieren.

Für nichtlineare Klassifizierung mit Big Data können Sie ein binäres Gauss-Kernel-Klassifizierungsmodell mit logistischer Regression mithilfe von fitckernel trainieren.

Blöcke

ClassificationLinear Predict

Classify observations using linear classification model (Seit R2023a)

Funktionen

Generalisierte lineare Regression mithilfe von `GeneralizedLinearModel`-Objekten

Erstellen von `GeneralizedLinearModel`-Objekten

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

Erstellen von `CompactGeneralizedLinearModel`-Objekten

compact Compact generalized linear regression model

Hinzufügen oder Entfernen von Termen zu/aus einem generalisierten linearen Modell

`addTerms`	Add terms to generalized linear regression model
`removeTerms`	Remove terms from generalized linear regression model
`step`	Improve generalized linear regression model by adding or removing terms

Vorhersagen von Antworten

`feval`	Predict responses of generalized linear regression model using one input for each predictor
`predict`	Predict responses of generalized linear regression model
`random`	Simulate responses with random noise for generalized linear regression model

Auswerten generalisierter linearer Modelle

`coefCI`	Confidence intervals of coefficient estimates of generalized linear regression model
`coefTest`	Linear hypothesis test on generalized linear regression model coefficients
`devianceTest`	Analysis of deviance for generalized linear regression model
`partialDependence`	Compute partial dependence

Visualisieren von generalisierten linearen Modellen und Übersichtsstatistiken

`plotDiagnostics`	Plot observation diagnostics of generalized linear regression model
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`plotResiduals`	Plot residuals of generalized linear regression model
`plotSlice`	Plot of slices through fitted generalized linear regression surface

Erfassen der Eigenschaften generalisierter linearer Modelle

gather Gather properties of Statistics and Machine Learning Toolbox object from GPU

Multinomiale logistische Regression mithilfe von `MultinomialRegression`-Objekten

Erstellen von `MultinomialRegression`-Objekten

fitmnr Fit multinomial regression model (Seit R2023a)

Arbeiten mit `MultinomialRegression`-Objekten

`coefCI`	Confidence intervals for coefficient estimates of multinomial regression model (Seit R2023a)
`coefTest`	Linear hypothesis test on multinomial regression model coefficients (Seit R2023a)
`feval`	Predict responses of multinomial regression model using one input for each predictor (Seit R2023a)
`partialDependence`	Compute partial dependence
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`plotResiduals`	Plot residuals of multinomial regression model (Seit R2023a)
`plotSlice`	Plot of slices through fitted multinomial regression surface (Seit R2023a)
`predict`	Predict responses of multinomial regression model (Seit R2023a)
`random`	Generate random responses from fitted multinomial regression model (Seit R2023a)
`testDeviance`	Deviance test for multinomial regression model (Seit R2023a)

Klassifizierung mit logistischer Regression

Erstellen von Objekten

`fitclinear`	Fit binary linear classifier to high-dimensional data
`fitcecoc`	Fit multiclass models for support vector machines or other classifiers
`fitckernel`	Fit binary Gaussian kernel classifier using random feature expansion
`templateLinear`	Linear learner template

Vorhersagen von Labels

`predict`	Predict labels for linear classification models
`predict`	Classify observations using multiclass error-correcting output codes (ECOC) model
`predict`	Predict labels for Gaussian kernel classification model

Generalisierte lineare Regression ohne Verwendung von Objekten

`glmfit`	Fit generalized linear regression model
`glmval`	Generalized linear model values

Objekte

Generalisierte lineare Regression

`GeneralizedLinearModel`	Generalized linear regression model class
`CompactGeneralizedLinearModel`	Compact generalized linear regression model class

Multinomiale Regression

MultinomialRegression Multinomial regression model (Seit R2023a)

Klassifizierung mit logistischer Regression

`ClassificationLinear`	Linear model for binary classification of high-dimensional data
`ClassificationECOC`	Multiclass model for support vector machines (SVMs) and other classifiers
`ClassificationKernel`	Gaussian kernel classification model using random feature expansion
`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

Themen

Generalisierte lineare Regression

Generalized Linear Models
Generalized linear models use linear methods to describe a potentially nonlinear relationship between predictor terms and a response variable.
Generalized Linear Model Workflow
Fit a generalized linear model and analyze the results.
Fitting Data with Generalized Linear Models
Fit and evaluate generalized linear models using glmfit and glmval.
Predict Class Labels Using ClassificationLinear Predict Block
This example shows how to use the ClassificationLinear Predict block for label prediction in Simulink®. (Seit R2023a)
Logistic Regression with Tall Arrays
This example shows how to use logistic regression and other techniques to perform data analysis on tall arrays.
Wilkinson Notation
Wilkinson notation provides a way to describe regression and repeated measures models without specifying coefficient values.

Multinomiale logistische Regression

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.
Multinomial Models for Hierarchical Responses
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.