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

Description Usage Arguments Value Author(s)

Fit a Bayesian Decision-Making Forest Classifier.

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

`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 each node. Defaults to `sqrt(p)`.
`alpha`	the feature sampling prior. Corresponds to alpha for a Dirichlet distribution. If `NULL`, samples uniformly for the initial training iteration.
`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`.
`train.params`	if you wish to provide specialized parameters for training, a named list containing the following named elements: `d` the number of features to subsample at a split node. `ntrees` the number of trees to construct. `bagg` the relative size of the subsamples from the training set. `depth.max` the maximum allowed tree depth. `size` the minimum allowed number of samples for an individual node. Any unset parameters will default to the values provided above (or the corresponding defaults if unprovided).
`...`	trailing arguments.

an object of class rf.class containing the following:

`forest`	A list a decision trees.
`method`	the method used to fit the forest.
`prior`	the hyperparameters of the Dirichlet Prior.
`post`	the hyperparamaters of the Dirichlet Posterior.