Construcción y evaluación de modelos

Selección de características, optimización de hiperparámetros, validación cruzada, diagnóstico sresidual, parcelas

Al crear un modelo de regresión de alta calidad, es importante seleccionar las entidades (o predictores) adecuados, ajustar los hiperparámetros (parámetros de modelo que no se ajustan a los datos) y evaluar las suposiciones del modelo mediante diagnósticos residuales.

Puede ajustar los hiperparámetros iterando entre elegir valores para ellos y validar un modelo mediante sus opciones. Este proceso produce varios modelos, y el mejor modelo entre ellos puede ser el que minimiza el error de generalización estimado. Por ejemplo, para ajustar un modelo de SVM, elija un conjunto de restricciones de cuadro y escalas de kernel, valide un modelo para cada par de valores y, a continuación, compare sus estimaciones de error de cuadrado medio validadas de 10 veces.

Ciertas funciones de regresión no paramétricas ofrecen además un ajuste automático de hiperparámetros a través de la optimización bayesiana, la búsqueda de cuadrículas o la búsqueda aleatoria.Statistics and Machine Learning Toolbox™ Sin embargo, , que es la función principal para implementar la optimización bayesiana, es lo suficientemente flexible para muchas otras aplicaciones.bayesopt Para obtener más información, consulte .Bayesian Optimization Workflow

Apps

	Train regression models to predict data using supervised machine learning
Alumno de regresión	Train regression models to predict data using supervised machine learning

Funciones

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Selección de características

`fsrnca`	Feature selection using neighborhood component analysis for regression
`oobPermutedPredictorImportance`	Predictor importance estimates by permutation of out-of-bag predictor observations for random forest of regression trees
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`predictorImportance`	Estimates of predictor importance for regression tree
`predictorImportance`	Estimates of predictor importance for regression ensemble
`relieff`	Rank importance of predictors using ReliefF or RReliefF algorithm
`sequentialfs`	Sequential feature selection using custom criterion
`stepwiselm`	Perform stepwise regression
`stepwiseglm`	Create generalized linear regression model by stepwise regression

Optimización de hiperparámetros

`bayesopt`	Select optimal machine learning hyperparameters using Bayesian optimization
`hyperparameters`	Variable descriptions for optimizing a fit function
`optimizableVariable`	Variable description for `bayesopt` or other optimizers

Validación cruzada

`crossval`	Estimate loss using cross-validation
`cvpartition`	Crear partición de validación cruzada para datos
`repartition`	Repartition data for cross-validation
`test`	Test indices for cross-validation
`training`	Training indices for cross-validation

Diagnóstico de modelos lineales

`coefCI`	Confidence intervals of coefficient estimates of linear regression model
`coefTest`	Linear hypothesis test on linear regression model coefficients
`dwtest`	Durbin-Watson test with linear regression model object
`plot`	Scatter plot or added variable plot of linear regression model
`plotAdded`	Added variable plot of linear regression model
`plotAdjustedResponse`	Adjusted response plot of linear regression model
`plotDiagnostics`	Plot observation diagnostics of linear regression model
`plotEffects`	Plot main effects of predictors in linear regression model
`plotInteraction`	Plot interaction effects of two predictors in linear regression model
`plotResiduals`	Plot residuals of linear regression model
`plotSlice`	Plot of slices through fitted linear regression surface

Diagnóstico generalizado del modelo lineal

`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
`plotDiagnostics`	Plot observation diagnostics of generalized linear regression model
`plotResiduals`	Plot residuals of generalized linear regression model
`plotSlice`	Plot of slices through fitted generalized linear regression surface

Diagnóstico de modelos no lineales

`coefCI`	Confidence intervals of coefficient estimates of nonlinear regression model
`coefTest`	Linear hypothesis test on nonlinear regression model coefficients
`plotDiagnostics`	Plot diagnostics of nonlinear regression model
`plotResiduals`	Plot residuals of nonlinear regression model
`plotSlice`	Plot of slices through fitted nonlinear regression surface

Pruebas de hipótesis lineales

linhyptest Linear hypothesis test

Objetos

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Selección de características

FeatureSelectionNCARegression Feature selection for regression using neighborhood component analysis (NCA)

Optimización de hiperparámetros

BayesianOptimization Bayesian optimization results

Validación cruzada

cvpartition

Temas

Flujo de trabajo de la aplicación Delearner de regresión

Train Regression Models in Regression Learner App

Workflow for training, comparing and improving regression models, including automated, manual, and parallel training.

Choose Regression Model Options

In Regression Learner, automatically train a selection of models, or compare and tune options of linear regression models, regression trees, support vector machines, Gaussian process regression models, ensembles of regression trees, and regression neural networks.

Feature Selection and Feature Transformation Using Regression Learner App

Identify useful predictors using plots, manually select features to include, and transform features using PCA in Regression Learner.

Assess Model Performance in Regression Learner

Compare model statistics and visualize results.

Selección de características

Introduction to Feature Selection

Learn about feature selection algorithms and explore the functions available for feature selection.

Sequential Feature Selection

This topic introduces to sequential feature selection and provides an example that selects features sequentially using a custom criterion and the sequentialfs function.

Neighborhood Component Analysis (NCA) Feature Selection

Neighborhood component analysis (NCA) is a non-parametric method for selecting features with the goal of maximizing prediction accuracy of regression and classification algorithms.

Robust Feature Selection Using NCA for Regression

Perform feature selection that is robust to outliers using a custom robust loss function in NCA.

Select Predictors for Random Forests

Select split-predictors for random forests using interaction test algorithm.

Optimización de hiperparámetros

Bayesian Optimization Workflow

Perform Bayesian optimization using a fit function or by calling bayesopt directly.

Variables for a Bayesian Optimization

Create variables for Bayesian optimization.

Bayesian Optimization Objective Functions

Create the objective function for Bayesian optimization.

Constraints in Bayesian Optimization

Set different types of constraints for Bayesian optimization.

Optimize a Boosted Regression Ensemble

Minimize cross-validation loss of a regression ensemble.

Bayesian Optimization Plot Functions

Visually monitor a Bayesian optimization.

Bayesian Optimization Output Functions

Monitor a Bayesian optimization.

Bayesian Optimization Algorithm

Understand the underlying algorithms for Bayesian optimization.

Parallel Bayesian Optimization

How Bayesian optimization works in parallel.

Validación cruzada

Implement Cross-Validation Using Parallel Computing

Speed up cross-validation using parallel computing.

Diagnóstico de modelos lineales

Interpretar resultados de regresión lineal

Mostrar e interpretar estadísticas de salida de regresión lineal.

Regresión lineal

Ajuste un modelo de regresión lineal y examine el resultado.

Linear Regression with Interaction Effects

Construct and analyze a linear regression model with interaction effects and interpret the results.

Summary of Output and Diagnostic Statistics

Evaluate a fitted model by using model properties and object functions.

F-estadística y t-estadística

En la regresión lineal, la estadística -statistic es la estadística de prueba para el enfoque de análisis de varianza (ANOVA) para probar la importancia del modelo o los componentes en el modelo.F La estadística -es útil para hacer inferencias sobre los coeficientes de regresión.t

Coeficiente de determinación (R-cuadrado)

El coeficiente de determinación (R cuadrado) indica la cantidad proporcional de variación en la variable de respuesta explicada por las variables independientes en el modelo de regresión lineal.yX

Coefficient Standard Errors and Confidence Intervals

Estimated coefficient variances and covariances capture the precision of regression coefficient estimates.

Residuals

Residuals are useful for detecting outlying y values and checking the linear regression assumptions with respect to the error term in the regression model.

Durbin-Watson Test

The Durbin-Watson test assesses whether or not there is autocorrelation among the residuals of time series data.

Cook’s Distance

Cook's distance is useful for identifying outliers in the X values (observations for predictor variables).

Hat Matrix and Leverage

The hat matrix provides a measure of leverage.

Delete-1 Statistics

Delete-1 change in covariance (CovRatio) identifies the observations that are influential in the regression fit.