buildEnsembleStack: Ensemble: Stacking

In SPOT: Sequential Parameter Optimization Toolbox

Ensemble: Stacking

Description

Generates an ensemble of surrogate models with stacking (stacked generalization).

Usage

buildEnsembleStack(x, y, control = list())

Arguments

x	design matrix (sample locations), rows for each sample, columns for each variable.
y	vector of observations at x
control	(list), with the options for the model building procedure: modelL1 Function for fitting the L1 model (default: buildLM) which combines the results of the L0 models. modelL1Control List of control parameters for the L1 model (default: list()). modelL0 A list of functions for fitting the L0 models (default: list(buildLM,buildRandomForest,buildKriging)). modelL0Control List of control lists for each L0 model (default: list(list(),list(),list())).

Value

returns an object of class ensembleStack.

Note

Loosely based on the code by Emanuele Olivetti https://github.com/emanuele/kaggle_pbr/blob/master/blend.py

References

Bartz-Beielstein, Thomas. Stacked Generalization of Surrogate Models-A Practical Approach. Technical Report 5/2016, TH Koeln, Koeln, 2016.

David H Wolpert. Stacked generalization. Neural Networks, 5(2):241-259, January 1992.

See Also

predict.ensembleStack

Examples

## Create design points
x <- cbind(runif(20)*15-5,runif(20)*15)
## Compute observations at design points
y <- funBranin(x)
## Create model with default settings
fit <- buildEnsembleStack(x,y)
## Predict new point
predict(fit,cbind(1,2))
## True value at location
funBranin(matrix( c(1,2), 1))

SPOT documentation built on June 26, 2022, 1:06 a.m.