| |
| |
| ``mx.callback.early.stop`` |
| ==================================================== |
| |
| Description |
| ---------------------- |
| |
| Early stop with different conditions. |
| |
| Early stopping applying different conditions: hard thresholds or epochs number from the best score. Tested with "epoch.end.callback" function. |
| |
| Usage |
| ---------- |
| |
| .. code:: r |
| |
| mx.callback.early.stop( |
| |
| train.metric = NULL, |
| |
| eval.metric = NULL, |
| |
| bad.steps = NULL, |
| |
| maximize = FALSE, |
| |
| verbose = FALSE |
| |
| ) |
| |
| Arguments |
| ------------------ |
| |
| +----------------------------------------+------------------------------------------------------------+ |
| | Argument | Description | |
| +========================================+============================================================+ |
| | ``train.metric`` | Numeric. Hard threshold for the metric of the training | |
| | | data set | |
| | | (optional) | |
| +----------------------------------------+------------------------------------------------------------+ |
| | ``eval.metric`` | Numeric. Hard threshold for the metric of the evaluating | |
| | | data set (if set, | |
| | | optional) | |
| +----------------------------------------+------------------------------------------------------------+ |
| | ``bad.steps`` | Integer. How much epochs should gone from the best score? | |
| | | Use this option with evaluation data | |
| | | set | |
| +----------------------------------------+------------------------------------------------------------+ |
| | ``maximize`` | Logical. Do your model use maximizing or minimizing | |
| | | optimization? | |
| +----------------------------------------+------------------------------------------------------------+ |
| | ``verbose`` | Logical | |
| +----------------------------------------+------------------------------------------------------------+ |
| |
| |
| |
