SoftForestPredFeeder
is used inside the user interface function to choose the appropriate depth function since the SDT's depth are not generated dynamically.
SoftForestPredFeeder(trainresponse, train, test, num.features, ntry, depth)
A vector of responses 0
and 1
for the training set with length equal to the number of observations in the training set.
A matrix or data frame consisting of all possible variables to attempt for the training set.
A matrix or data frame consisting of all possible variables to attempt for the test set.
The number of variables in the dataset to possibly try. The leftmost number of variables in the dataset are the variables chosen.
The number of variables from the num.features
to attempt to split. This is useful for building random forests. For a standard tree, choose ntry = num.features
.
The number of the depth each SDT should be. Here this ends with \(2^{depth - 1}\) terminal nodes.
The output from the chosen function.
SoftForestPredDepth
chooses the correct of five possible depths that has functioning code. Any invalid attempt returns an error.