가우스 과정 회귀

가우스 과정 회귀 모델(크리깅 보간)

가우스 과정 회귀(GPR) 모델은 비모수 커널 기반의 확률적 모델입니다. GPR 모델을 대화형 방식으로 훈련시키려면 회귀 학습기 앱을 사용하십시오. 명령줄에서 fitrgp 함수를 사용하여 GPR 모델을 훈련시키면 유연성을 높일 수 있습니다. 훈련 후에는 모델과 새로운 예측 변수 데이터를 predict 객체 함수에 전달하여 새로운 데이터에 대한 응답 변수를 예측할 수 있습니다.

앱

회귀 학습기

머신러닝 지도 학습을 사용하여 데이터를 예측하도록 회귀 모델 훈련시키기

블록

RegressionGP Predict

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

함수

모두 확장

가우스 과정 회귀 모델 또는 템플릿 만들기

`fitrgp`	가우스 과정 회귀(GPR) 모델 피팅
`compact`	Reduce size of machine learning model
`templateGP`	Gaussian process template (R2023b 이후)

예측 해석하기

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

교차 검증하기

`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

성능 측정하기

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

응답 변수 예측하기

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

객체

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

도움말 항목

가우스 과정 회귀 모델
가우스 과정 회귀(GPR) 모델은 비모수 커널 기반의 확률적 모델입니다.
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.