CRTree_Forest_pred_internals: Complete-Random Tree Forest Predictor (Deferred predictor)...
In Laurae2/Laurae: Advanced High Performance Data Science Toolbox for R

Description Usage Arguments Details Value

This function attempts to predict from Complete-Random Tree Forests using xgboost. Requesting predictions form CRTreeForest should be done using CRTreeForest_pred.

1 2	CRTree_Forest_pred_internals(model, data, folds = NULL, prediction = FALSE, multi_class = NULL, data_start = NULL, return_list = TRUE)

`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`.

For implementation details of Cascade Forest / Complete-Random Tree Forest / Multi-Grained Scanning / Deep Forest, check this: https://github.com/Microsoft/LightGBM/issues/331#issuecomment-283942390 by Laurae.

A data.table or a list based on data predicted using model.

Laurae2/Laurae documentation built on May 8, 2019, 7:59 p.m.