# Regresión del proceso gaussiano

Modelos de regresión de procesos gaussianos (kriging)

## Apps

 Train regression models to predict data using supervised machine learning Alumno de regresión Train regression models to predict data using supervised machine learning

## Funciones

 `fitrgp` Ajustar un modelo de regresión del proceso gaussiano (GPR) `predict` Predict response of Gaussian process regression model `loss` Regression error for Gaussian process regression model `compact` Create compact Gaussian process regression model `crossval` Cross-validate Gaussian process regression model `plotPartialDependence` Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots `postFitStatistics` Compute post-fit statistics for the exact Gaussian process regression model `resubLoss` Resubstitution loss for a trained Gaussian process regression model `resubPredict` Resubstitution prediction from a trained Gaussian process regression model

## Clases

 `RegressionGP` Gaussian process regression model class `CompactRegressionGP` Compact Gaussian process regression model class

## Temas

Modelos de regresión de procesos gaussianos

Los modelos de regresión de procesos gaussianos (GPR) son modelos probabilísticos no paramétricos basados en kernel.

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.