predict.BTCVFit: Predictions for CV fitted BT models.

In BT: (Adaptive) Boosting Trees Algorithm

Predictions for CV fitted BT models.

Description

Compute predictions from cross-validated Boosting Trees model.

Usage

## S3 method for class 'BTCVFit'
predict(object, data, cv.folds, folds, best.iter.cv, ...)

Arguments

object	a BTCVFit object containing CV BT models.
data	the database on which one wants to predict the different CV BT models.
cv.folds	a positive integer specifying the number of folds to be used in cross-validation of the BT fit.
folds	vector of integers specifying which row of data belongs to which cv.folds.
best.iter.cv	the optimal number of trees with a CV approach.
...	not currently used.

Details

This function has not been coded for public usage but rather to assess the cross-validation performances.

Value

Returns a vector of predictions for each cv folds.

Author(s)

Gireg Willame [e-mail]

This package is inspired by the gbm3 package. For more details, see https://github.com/gbm-developers/gbm3/.

References

M. Denuit, D. Hainaut and J. Trufin (2019). Effective Statistical Learning Methods for Actuaries |: GLMs and Extensions, Springer Actuarial.

M. Denuit, D. Hainaut and J. Trufin (2019). Effective Statistical Learning Methods for Actuaries ||: Tree-Based Methods and Extensions, Springer Actuarial.

M. Denuit, D. Hainaut and J. Trufin (2019). Effective Statistical Learning Methods for Actuaries |||: Neural Networks and Extensions, Springer Actuarial.

M. Denuit, D. Hainaut and J. Trufin (2022). Response versus gradient boosting trees, GLMs and neural networks under Tweedie loss and log-link. Accepted for publication in Scandinavian Actuarial Journal.

M. Denuit, J. Huyghe and J. Trufin (2022). Boosting cost-complexity pruned trees on Tweedie responses: The ABT machine for insurance ratemaking. Paper submitted for publication.

M. Denuit, J. Trufin and T. Verdebout (2022). Boosting on the responses with Tweedie loss functions. Paper submitted for publication.

See Also

BT, BTFit.

BT documentation built on Aug. 19, 2023, 5:09 p.m.