Desarrollo y evaluación de modelos

Selección de características, ingeniería de características, selección de modelos, optimización de hiperparámetros, validación cruzada, diagnósticos de valores residuales y gráficas

Al desarrollar un modelo de regresión de alta calidad, es importante seleccionar las características (o predictores) correctos, ajustar los hiperparámetros (parámetros del modelo no ajustados a los datos) y evaluar los supuestos del modelo a través de diagnósticos de valores residuales.

Puede ajustar los hiperparámetros mediante iteraciones entre la selección de los valores para los mismos y la realización de una validación cruzada de un modelo usando sus propias opciones. Este proceso arroja varios modelos y el mejor de ellos puede ser el que minimice el error de generalización estimado. Por ejemplo, para ajustar un modelo SVM, elija un conjunto de restricciones de cajas y escalas de kernel, realice una validación cruzada de un modelo para cada par de valores y, después, compare las estimaciones de los cuadrados de error medios con validación cruzada de 10 particiones.

Para extraer nuevas características antes de entrenar un modelo de regresión, utilice genrfeatures.

Para desarrollar y evaluar modelos de regresión de forma interactiva, utilice la app Regression Learner.

Para seleccionar automáticamente un modelo con hiperparámetros ajustados, utilice fitrauto. La función prueba una selección de tipos de modelos de regresión con diferentes valores en los hiperparámetros y devuelve un modelo final que se prevé que funcione bien. Utilice fitrauto cuando no sepa con seguridad los tipos de modelos de regresión que mejor se adaptan a sus datos.

Determinadas funciones de regresión no paramétricas de Statistics and Machine Learning Toolbox™ ofrecen un ajuste automático de los hiperparámetros mediante optimización bayesiana, búsqueda por cuadrículas o búsqueda aleatoria. bayesopt, la función principal para implementar la optimización bayesiana, es también lo suficientemente flexible para muchas otras aplicaciones. Para obtener más información, consulte Bayesian Optimization Workflow.

Para interpretar un modelo de regresión, puede utilizar lime, shapley y plotPartialDependence.

Apps

Regression Learner

Entrenar modelos de regresión para predecir datos usando machine learning supervisado

Funciones

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Selección de características

`fsrftest`	Univariate feature ranking for regression using F-tests (desde R2020a)
`fsrmrmr`	Rank features for regression using minimum redundancy maximum relevance (MRMR) algorithm (desde R2022a)
`fsrnca`	Feature selection using neighborhood component analysis for regression
`oobPermutedPredictorImportance`	Out-of-bag predictor importance estimates for random forest of regression trees by permutation
`partialDependence`	Compute partial dependence (desde R2020b)
`permutationImportance`	Predictor importance by permutation (desde R2024a)
`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 of decision trees
`relieff`	Rank importance of predictors using ReliefF or RReliefF algorithm
`selectFeatures`	Select important features for NCA classification or regression (desde R2023b)
`sequentialfs`	Sequential feature selection using custom criterion
`stepwiselm`	Perform stepwise regression
`stepwiseglm`	Create generalized linear regression model by stepwise regression

Ingeniería de características

`genrfeatures`	Perform automated feature engineering for regression (desde R2021b)
`describe`	Describe generated features (desde R2021a)
`transform`	Transform new data using generated features (desde R2021a)

Selección de modelos automatizados

fitrauto Automatically select regression model with optimized hyperparameters (desde R2020b)

Optimización de hiperparámetros

`bayesopt`	Select optimal machine learning hyperparameters using Bayesian optimization
`hyperparameters`	Variable descriptions for optimizing a fit function
`optimizableVariable`	Descripción de variables para `bayesopt` u otros optimizadores

Validación cruzada

Para datos no dependientes del tiempo

`crossval`	Estimate loss using cross-validation
`cvpartition`	Partición de datos para validación cruzada
`repartition`	Repartition data for cross-validation
`test`	Índices de prueba para la validación cruzada
`training`	Índices de entrenamiento para la validación cruzada

Para datos de series temporales

`tspartition`	Partition time series data for cross-validation (desde R2022b)
`test`	Test indices for time series cross-validation (desde R2022b)
`training`	Training indices for time series cross-validation (desde R2022b)

Interpretación de modelos

Explicaciones independientes del modelo local interpretable (LIME, por sus siglas en inglés)

`lime`	Local interpretable model-agnostic explanations (LIME) (desde R2020b)
`fit`	Fit simple model of local interpretable model-agnostic explanations (LIME) (desde R2020b)
`plot`	Plot results of local interpretable model-agnostic explanations (LIME) (desde R2020b)

Valores de Shapley

`shapley`	Shapley values (desde R2021a)
`fit`	Compute Shapley values for query points (desde R2021a)
`plot`	Plot Shapley values using bar graphs (desde R2021a)
`boxchart`	Visualize Shapley values using box charts (box plots) (desde R2024a)
`swarmchart`	Visualize Shapley values using swarm scatter charts (desde R2024a)

Dependencia parcial

`partialDependence`	Compute partial dependence (desde R2020b)
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots

Diagnósticos 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

Diagnósticos de modelos lineales generalizados

`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

Diagnósticos 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
`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)

Ingeniería de características

FeatureTransformer Generated feature transformations (desde R2021a)

Optimización de hiperparámetros

BayesianOptimization Bayesian optimization results

Temas

Flujo de trabajo de la app Regression Learner

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, kernel approximation models, ensembles of regression trees, and regression neural networks.
Selección y transformación de características mediante la app Regression Learner
Identifique predictores útiles utilizando gráficas o algoritmos de clasificación de características, seleccione las características que desee incluir y transfórmelas con el PCA en Regression Learner.
Visualizar y evaluar el rendimiento de modelos en Regression Learner
Compare las métricas de los modelos y visualice los resultados.

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 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.

Ingeniería de características

Automated Feature Engineering for Regression
Use genrfeatures to engineer new features before training a regression model. Before making predictions on new data, apply the same feature transformations to the new data set.

Selección de modelos automatizados

Automated Regression Model Selection with Bayesian and ASHA Optimization
Use fitrauto to automatically try a selection of regression model types with different hyperparameter values, given training predictor and response data.

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.

Interpretación de modelos

Interpret Machine Learning Models
Explain model predictions using the lime and shapley objects and the plotPartialDependence function.
Shapley Values for Machine Learning Model
Compute Shapley values for a machine learning model using interventional algorithm or conditional algorithm.
Shapley Output Functions
Stop Shapley computations, create plots, save information to your workspace, or perform calculations while using shapley.

Validación cruzada

Implement Cross-Validation Using Parallel Computing
Speed up cross-validation using parallel computing.
Perform Time Series Direct Forecasting with directforecaster
Perform time series direct forecasting with the directforecaster function.
Manually Perform Time Series Forecasting Using Ensembles of Boosted Regression Trees
Manually perform single-step and multiple-step time series forecasting with ensembles of boosted regression trees.

Diagnósticos de modelos lineales

Interpret Linear Regression Results
Display and interpret linear regression output statistics.
Linear Regression
Fit a linear regression model and examine the result.
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-statistic and t-statistic
In linear regression, the F-statistic is the test statistic for the analysis of variance (ANOVA) approach to test the significance of the model or the components in the model. The t-statistic is useful for making inferences about the regression coefficients.
Coeficiente de determinación (R cuadrado)
El coeficiente de determinación (R cuadrado) indica la cantidad proporcional de variación en la variable de respuesta y explicada por las variables independientes X en el modelo de regresión lineal.
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.

Diagnósticos de modelos lineales generalizados

Generalized Linear Models
Generalized linear models use linear methods to describe a potentially nonlinear relationship between predictor terms and a response variable.

Diagnósticos de modelos no lineales

Nonlinear Regression
Parametric nonlinear models represent the relationship between a continuous response variable and one or more continuous predictor variables.