module IntMap = BatMap.Int
type features = int IntMap.t
type forest = (tree * int array) array
val train : int -> Random.State.t -> metric -> int -> RFC.int_or_float -> int -> RFC.int_or_float -> int -> sample array -> forest
train ncores rng metric ntrees max_features card_features
max_samples min_node_size training_set
(pred_avg, pred_std_dev) = predict_one ncores trained_forest sample
predict_one but for an array of samples
use a trained forest to predict on the Out Of Bag (OOB) training set of each tree. The training_set must be provided in the same order than when the model was trained. Can be used to get a reliable model performance estimate, even if you don't have a left out test set.
truth_preds = predict_OOB forest training_set
Save model to file (Marshal). OOB samples are dropped prior to saving the model.