Regressione con processo gaussiano

Modelli di regressione basati sul processo gaussiano (kriging)

I modelli di regressione basati sul processo gaussiano (GPR) sono modelli probabilistici non parametrici basati sul kernel. Per addestrare un modello GPR in modo interattivo, utilizzare l'app Regression Learner. Per una maggiore flessibilità, addestrare un modello GPR utilizzando la funzione fitrgp dalla riga di comando. Dopo l'addestramento, è possibile prevedere le risposte per nuovi dati passando il modello e i nuovi dati predittori alla funzione oggetto predict.

App

Regression Learner

Train regression models to predict data using supervised machine learning

Blocchi

RegressionGP Predict

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

Funzioni

espandi tutto

Creazione di un modello o di un template di regressione basato sul processo gaussiano

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

Interpretazione della previsione

`lime`	Local interpretable model-agnostic explanations (LIME)
`partialDependence`	Compute partial dependence
`permutationImportance`	Predictor importance by permutation (Da R2024a)
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`shapley`	Shapley values

Validazione incrociata

`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

Misurazione delle prestazioni

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

Previsione delle risposte

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

Utilizzo delle catene di regressione per la regressione multirisposta

`fitrchains`	Multiresponse regression with regression chains (Da R2024b)
`compact`	Reduce size of multiresponse regression model (Da R2024b)
`loss`	Loss for multiresponse regression model (Da R2024b)
`predict`	Predict responses using multiresponse regression model (Da R2024b)

Oggetti

`RegressionGP`	Gaussian process regression model
`CompactRegressionGP`	Compact Gaussian process regression model class
`RegressionPartitionedGP`	Cross-validated Gaussian process regression (GPR) model (Da R2022b)
`RegressionChainEnsemble`	Multiresponse regression model (Da R2024b)
`CompactRegressionChainEnsemble`	Compact multiresponse regression model (Da R2024b)

Argomenti

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.

Esempi in primo piano

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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