|回归学习器||Train regression models to predict data using supervised machine learning|
|Fit a Gaussian process regression (GPR) model|
|Predict response of Gaussian process regression model|
|Regression error for Gaussian process regression model|
|Reduce size of machine learning model|
|Cross-validate machine learning model|
|Local interpretable model-agnostic explanations (LIME)|
|Compute partial dependence|
|Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots|
|Compute post-fit statistics for the exact Gaussian process regression model|
|Resubstitution regression loss|
|Predict responses for training data using trained regression model|
- 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.