Module type
Class type
type filename = string
type mode =
  1. | Regression
  2. | Classification
val train : ?debug:bool -> ?nprocs:int -> mode -> int -> int option -> filename -> string -> filename -> bool

train ~debug ~nprocs mode nb_trees mtry data_fn dep_var_name out_fn will train a Random Forests model with nb_trees, optionally using mtry features to split at each node (sqrt(|features|) if None), reading training data from data_fn and using mode (either Regression or Classification). dep_var_name is the name of the column holding the target value (you want to predict that value with your model later on) in data_fn. data_fn is a space-separated CSV file, with first line as its header (i.e. the names of all columns). If training in parallel (nprocs > 1) then nprocs threads are used. The trained model will be stored in out_fn. The debug flag controls the verbosity of the underlying C++ software (ranger) which is really doing all the work.

val predict : ?debug:bool -> ?nprocs:int -> int -> filename -> filename -> (float * float) list option

predict ~debug ~nprocs nb_trees data_fn model_fn will optionally return a list of (pred_val, stddev). I.e. predicted values along with their standard deviation. The debug flag controls the verbosity of the underlying C++ software (ranger). If predicting in parallel (nprocs > 1), then nprocs threads are used. nb_trees is the number of trees of your trained model. data_fn is the CSV file holding your test data. The column in data_fn holding the target value will be ignored. model_fn is a file where you previously stored a (trained) model.