[ML] Prefer smaller models with similar performance #1516
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valeriy42
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@wwang500 just a heads-up: in some cases this may lead to a regression of the results by <1% (in terms of MSE, MSLE, Huber) or less (for classification). |
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For regression and classification, during hyperparameter optimization we prefer smaller models if the loss functions are otherwise comparable. To this end, we add 0.01 * "forest number nodes" * E[GP] / "average forest number nodes" as an additional penalty.
valeriy42
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For regression and classification, during hyperparameter optimization we prefer smaller models if the loss functions are otherwise comparable. To this end, we add 0.01 * "forest number nodes" * E[GP] / "average forest number nodes" as an additional penalty. Backport of #1516.
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For regression and classification, during hyperparameter optimization we prefer smaller models if the loss functions are otherwise comparable.
To this end, we add 0.01 * "forest number nodes" * E[GP] / "average forest number nodes" as an additional penalty.