View source: R/logic.boosting.R
bestBoostingIter | R Documentation |
This function can be used to compute the ideal number of boosting iterations
for the fitted logic.boosted
model using independent validation data.
bestBoostingIter(model, X, y, Z = NULL, consec.iter = 5, scoring_rule = "auc")
model |
Fitted |
X |
Matrix or data frame of binary validation input data. This object should correspond to the binary matrix for fitting the model. |
y |
Validation response vector. 0-1 coding for binary outcomes. |
Z |
Optional quantitative covariables supplied as a matrix or data frame. Only used (and required) if the model was fitted using them. |
consec.iter |
Number of consecutive boosting iterations that do not increase the validation performance for determining the ideal number of iterations |
scoring_rule |
Scoring rule computing the validation performance. This can either be "auc" for the area under the receiver operating characteristic curve (default for binary reponses), "deviance" for the deviance, "nce" for the normalized cross entropy or "brier" for the Brier score. For regression purposes, the MSE (mean squared error) is automatically chosen. |
If the model performance (on the validation data) cannot be increased for
consec.iter
consecutive boosting iterations, the last iteration
which increased the validation performance induces the ideal number of
boosting iterations.
The ideal number of boosting iterations
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