cb.early.stop
From xgboost v0.6-3
by Tong He
Callback closure to activate the early stopping.
Callback closure to activate the early stopping.
Usage
cb.early.stop(stopping_rounds, maximize = FALSE, metric_name = NULL, verbose = TRUE)Arguments
- stopping_rounds
- The number of rounds with no improvement in the evaluation metric in order to stop the training.
- maximize
- whether to maximize the evaluation metric
- metric_name
- the name of an evaluation column to use as a criteria for early
stopping. If not set, the last column would be used.
Let's say the test data in
watchlistwas labelled asdtest, and one wants to use the AUC in test data for early stopping regardless of where it is in thewatchlist, then one of the following would need to be set:metric_name='dtest-auc'ormetric_name='dtest_auc'. All dash '-' characters in metric names are considered equivalent to '_'. - verbose
- whether to print the early stopping information.
Details
This callback function determines the condition for early stopping
by setting the stop_condition = TRUE flag in its calling frame.
The following additional fields are assigned to the model's R object:
-
best_scorethe evaluation score at the best iteration -
best_iterationat which boosting iteration the best score has occurred (1-based index) -
best_ntreelimitto use with thentreelimitparameter inpredict. It differs frombest_iterationin multiclass or random forest settings.
The Same values are also stored as xgb-attributes:
-
best_iterationis stored as a 0-based iteration index (for interoperability of binary models) -
best_msgmessage string is also stored.
At least one data element is required in the evaluation watchlist for early stopping to work.
Callback function expects the following values to be set in its calling frame:
stop_condition,
bst_evaluation,
rank,
bst (or bst_folds and basket),
iteration,
begin_iteration,
end_iteration,
num_parallel_tree.
See Also
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