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Regresión de procesos gaussianos

Modelos de regresión de procesos gaussianos (kriging)

Los modelos de regresión de procesos gaussianos (GPR) son modelos probabilísticos no paramétricos basados en kernels. Para entrenar un modelo GPR de forma interactiva, utilice la app Regression Learner. Para mayor flexibilidad, entrene un modelo GPR utilizando la función fitrgp en la línea de comandos. Tras el entrenamiento, puede predecir las respuestas con los nuevos datos pasando el modelo y los nuevos datos de los predictores a la función predict del objeto.

Apps

Regression Learner

Entrenar modelos de regresión para predecir datos usando machine learning supervisado

Bloques

RegressionGP Predict

Predict responses using Gaussian process (GP) regression model (desde R2022a)

Funciones

expandir todo

Crear un modelo o plantilla de regresión de procesos gaussianos

`fitrgp`	Fit a Gaussian process regression (GPR) model
`compact`	Reduce size of machine learning model
`templateGP`	Gaussian process template (desde R2023b)

Interpretar una predicción

`lime`	Local interpretable model-agnostic explanations (LIME) (desde R2020b)
`partialDependence`	Compute partial dependence (desde R2020b)
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`shapley`	Shapley values (desde R2021a)

Realizar una validación cruzada

`crossval`	Cross-validate machine learning model
`kfoldLoss`	Loss for cross-validated partitioned regression model
`kfoldPredict`	Predict responses for observations in cross-validated regression model
`kfoldfun`	Cross-validate function for regression

Medir el rendimiento

`loss`	Regression error for Gaussian process regression model
`resubLoss`	Resubstitution regression loss
`postFitStatistics`	Compute post-fit statistics for the exact Gaussian process regression model

Predecir respuestas

`predict`	Predict response of Gaussian process regression model
`resubPredict`	Predict responses for training data using trained regression model

Objetos

`RegressionGP`	Gaussian process regression model
`CompactRegressionGP`	Compact Gaussian process regression model class
`RegressionPartitionedGP`	Cross-validated Gaussian process regression (GPR) model (desde R2022b)

Temas

Gaussian Process Regression Models
Gaussian process regression (GPR) models are nonparametric kernel-based probabilistic models.
Kernel (Covariance) Function Options
In Gaussian processes, the covariance function expresses the expectation that points with similar predictor values will have similar response values.
Exact GPR Method
Learn the parameter estimation and prediction in exact GPR method.
Subset of Data Approximation for GPR Models
With large data sets, the subset of data approximation method can greatly reduce the time required to train a Gaussian process regression model.
Subset of Regressors Approximation for GPR Models
The subset of regressors approximation method replaces the exact kernel function by an approximation.
Fully Independent Conditional Approximation for GPR Models
The fully independent conditional (FIC) approximation is a way of systematically approximating the true GPR kernel function in a way that avoids the predictive variance problem of the SR approximation while still maintaining a valid Gaussian process.
Block Coordinate Descent Approximation for GPR Models
Block coordinate descent approximation is another approximation method used to reduce computation time with large data sets.
Predict Responses Using RegressionGP Predict Block
Train a Gaussian process (GP) regression model, and then use the RegressionGP Predict block for response prediction.

Ejemplos destacados

Predict Battery State of Charge Using Machine Learning

Train a Gaussian process regression model to predict the state of charge of a battery in automotive engineering.

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