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高斯过程回归

高斯过程回归模型 (kriging)

App

回归学习器Train regression models to predict data using supervised machine learning

函数

fitrgpFit a Gaussian process regression (GPR) model
predictPredict response of Gaussian process regression model
lossRegression error for Gaussian process regression model
compact
crossval
limeLocal interpretable model-agnostic explanations (LIME)
partialDependenceCompute partial dependence
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots
postFitStatisticsCompute post-fit statistics for the exact Gaussian process regression model
resubLoss
resubPredict
shapleyShapley values

RegressionGPGaussian process regression model class
CompactRegressionGPCompact Gaussian process regression model class

主题

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.