高斯过程回归

高斯过程回归模型 (kriging)

高斯过程回归 (GPR) 模型是基于核的非参数化概率模型。要以交互方式训练 GPR 模型，请使用回归学习器。要获得更大的灵活性，请在命令行中使用 fitrgp 函数来训练 GPR 模型。训练后，您可以通过将模型和新预测变量数据传递给 predict 对象函数来预测新数据的响应。

App

使用有监督机器学习训练回归模型来预测数据

Predict responses using Gaussian process (GP) regression model (自 R2022a 起)

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

`lime`	Local interpretable model-agnostic explanations (LIME) (自 R2020b 起)
`partialDependence`	Compute partial dependence (自 R2020b 起)
`permutationImportance`	Predictor importance by permutation (自 R2024a 起)
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`shapley`	Shapley values (自 R2021a 起)

`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

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

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

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

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.