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

Description Usage Arguments Value Author(s)

Fit a Random Forest with a 'stats'-like formula frontend interface.

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rf.fit(formuler, data = NULL, d = NULL, alpha = NULL, ntrees = 10L,
  bagg = 0.632, method = "classification", depth.max = 5L,
  size = 1L, debug = FALSE, mc.cores = 1L, ...)

`formuler`	ravioli ravioli give me the formuoli.
`data`	the data associated with the formuler. Note: if you want an intercept, you must add it ahead of time.
`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. Should be a `[p]` vector, where `p` is the number of predictors. 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*nsamples` elements. Defaults to `0.632`.
`method`	whether you want "classification" or "regression".
`depth.max`	the maximum allowed tree depth.
`size`	the minimum allowed number of samples for an individual node.
`debug`	whether to save the predictors and responses that are categorized
`mc.cores`	the number of cores to use. Should be `0 < mc.cores <= parallel::detectCores()`. Any unset parameters will default to the values provided above (or the corresponding defaults if unprovided).