predict.spotRandomForest

fit of the model, an object of class <code>"spotRandomForest"</code>, produced by <code><a rd-options="" href="/link/buildRandomForest?package=SPOT&version=2.11.10" data-mini-rdoc="SPOT::buildRandomForest">buildRandomForest</a></code>.

object

newdata

Wrapper for <code>predict.randomForest</code>.

internal

A set of tools for model-based optimization and tuning of
algorithms (hyperparameter tuning respectively hyperparameter optimization). It includes surrogate models, optimizers, and design of experiment
approaches. The main interface is spot, which uses sequentially updated
surrogate models for the purpose of efficient optimization. The main goal is
to ease the burden of objective function evaluations, when a single evaluation
requires a significant amount of resources.

Thomas Bartz-Beielstein

SPOT

Sequential Parameter Optimization Toolbox

Martin Zaefferer

Frederik Rehbach

Margarita Rebolledo

Joerg Stork

Christian Lasarczyk

predict.spotRandomForest function

fit of the model, an object of class <code>"spotRandomForest"</code>, produced by <code><a rd-options='' href='buildRandomForest'>buildRandomForest</a></code>.

predict.spotRandomForest: Prediction method for random forest

Description

Usage

Arguments