predict.BTCVFit {BT} | R Documentation |
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 |
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 gireg.willame@gmail.com
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.