fastForest: fastForest

Creates a list containing the function name and arguments to train a FastForest model with rxEnsemble.

Usage

  fastForest(numTrees = 100, numLeaves = 20, minSplit = 10,
    exampleFraction = 0.7, featureFraction = 0.7, splitFraction = 0.7,
    numBins = 255, firstUsePenalty = 0, gainConfLevel = 0,
    trainThreads = 8, randomSeed = NULL, ...)
 

Arguments

numTrees

Specifies the total number of decision trees to create in the ensemble. By creating more decision trees, you can potentially get better coverage, but the training time increases. The default value is 100.

numLeaves

The maximum number of leaves (terminal nodes) that can be created in any tree. Higher values potentially increase the size of the tree and get better precision, but risk overfitting and requiring longer training times. The default value is 20.

minSplit

Minimum number of training instances required to form a leaf. That is, the minimal number of documents allowed in a leaf of a regression tree, out of the sub-sampled data. A 'split' means that features in each level of the tree (node) are randomly divided. The default value is 10.

exampleFraction

The fraction of randomly chosen instances to use for each tree. The default value is 0.7.

featureFraction

The fraction of randomly chosen features to use for each tree. The default value is 0.7.

splitFraction

The fraction of randomly chosen features to use on each split. The default value is 0.7.

numBins

Maximum number of distinct values (bins) per feature. The default value is 255.

firstUsePenalty

The feature first use penalty coefficient. The default value is 0.

gainConfLevel

Tree fitting gain confidence requirement (should be in the range [0,1)). The default value is 0.

trainThreads

The number of threads to use in training. If NULLis specified, the number of threads to use is determined internally. The default value is NULL.

randomSeed

Specifies the random seed. The default value is NULL.

...

Additional arguments.