Arguments
model
Type: list. A model trained by CRTreeForest.
data
Type: data.table. A data to predict on. If passing training data, it will predict as if it was out of fold and you will overfit (so, use the list train_preds instead please).
folds
Type: list. The folds as list for cross-validation if using the training data. Otherwise, leave NULL. Defaults to NULL.
prediction
Type: logical. Whether the predictions of the forest ensemble are averaged. Set it to FALSE for debugging / feature engineering. Setting it to TRUE overrides return_list. Defaults to FALSE.
multi_class
Type: numeric. How many classes you got. Set to 2 for binary classification, or regression cases. Set to NULL to let it try guessing by reading the model. Defaults to NULL.
data_start
Type: vector of numeric. The initial prediction labels. Set to NULL if you do not know what you are doing. Defaults to NULL.
return_list
Type: logical. Whether lists should be returned instead of concatenated frames for predictions. Defaults to TRUE.