Comments (2)
+1 this is a more natural strategy.
For the second point, you mean updating the training loss while we update the gradients to avoid redundant computations? This seems doable for LS at least.
I'll go for a simple version first (separate updates) and see what it looks like.
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For the second point, you mean updating the training loss while we update the gradients to avoid redundant computations? This seems doable for LS at least.
Yes.
I'll go for a simple version first (separate updates) and see what it looks like.
Alright.
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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
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- 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
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