rf.class.fit: Random Forest (RF) for Classification
In ebridge2/badmf: Bayesian Decision-Making Forests (BaD-MFs)

Fit a Random Forest Classifier.

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rf.class.fit(X, Y, d = NULL, alpha = NULL, ntrees = 10L,
  bagg = 0.632, depth.max = 5L, size = 1L, debug = FALSE,
  mc.cores = 1L, ...)

`X`	the predictors. A `[n, p]` matrix.
`Y`	the responses. A `[n]` vector or, optionally, a factor.
`d`	the number of features to subsample at a split node. Defaults to `as.integer(round(sqrt(p)))`, where `p` is the dimensionality of the predictor feature space.
`alpha`	the feature sampling prior. Corresponds to alpha for a Dirichlet distribution. If `NULL`, samples uniformly.
`ntrees`	the number of trees to construct. Defaults to `10L`.
`bagg`	the relative size of the subsamples for the training set. A numeric s.t. `0 < bagg <= 1`. Each subsample will be `bagg*n` elements. Defaults to `0.632`.
`depth.max`	the maximum allowed tree depth. Defaults to `5L`.
`size`	the minimum allowed number of samples for an individual node. Defaults to `1L`.
`debug`	whether to save the predictors and responses that are categorized. Defaults to `FALSE`.
`mc.cores`	the number of cores to use. Should be `0 < mc.cores <= parallel::detectCores()`. Defaults to `1L`.