Comments (1)
One solution is to get rid of scoring and check early stopping based on the loss.
Being able to enable early stopping based on the loss would be interesting and could be made the default behavior (that is, have scoring=None
by default in the constructor).
However, I think it's interesting to make it possible to use a pluggable "business" metric that is not the loss for early stopping as early stopping is a form of model selection and it makes sense to use the "business" metric for that.
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Related Issues (20)
- API documentation is broken HOT 1
- All the examples require lightgbm HOT 1
- Allow score monitoring regardless of early stopping
- Optimize score loss computation
- Remove empty slice check (numba fixed the issue)
- Reuse grower (and thus the splitter) instead of creating a new one
- Updating to Scipy 1.2.0 breaks loss tests... HOT 2
- Optionally use left/right indices buffer HOT 7
- Avoid ordered_gradients? HOT 7
- Remove constant_hessian_value? HOT 1
- sum_gradient and sum_hessians computation in find_node_split_subtraction HOT 4
- Optimize categorical crossentropy gradient update HOT 3
- _update_raw_predictions() throws a deprecation warning HOT 1
- numba-integration-test failure HOT 6
- Status of this project? HOT 2
- Implement native support for missing values
- did you stopped development since since can not do better than lightGBM pr Xgboost pr catboost? HOT 4
- Implement histogram recycling to improve memory efficiency
- Recent Numba not usable with pygbm HOT 1
- Parallel splitting fails in nopython mode
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