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Regresión de procesos gaussianos
Apps
Regression Learner | Train regression models to predict data using supervised machine learning |
Funciones
fitrgp | Fit a Gaussian process regression (GPR) model |
predict | Predict response of Gaussian process regression model |
loss | Regression error for Gaussian process regression model |
compact | Reduce size of machine learning model |
crossval | Cross-validate machine learning model |
lime | Local interpretable model-agnostic explanations (LIME) |
partialDependence | Compute partial dependence |
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 regression loss |
resubPredict | Predict responses for training data using trained regression model |
shapley | Shapley values |
Clases
RegressionGP | Gaussian process regression model class |
CompactRegressionGP | Compact Gaussian process regression model class |
Temas
- 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.