[ML] Stop cross-validation early if the parameters have high predicted test loss #915
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valeriy42
reviewed
Jan 9, 2020
Co-Authored-By: Valeriy Khakhutskyy <1292899+valeriy42@users.noreply.github.com>
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Thanks for the review @valeriy42. I think I've now addressed all your comments. |
valeriy42
approved these changes
Jan 10, 2020
lib/maths/CBoostedTreeFactory.cc
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| @@ -46,7 +46,10 @@ const double MIN_DOWNSAMPLE_LINE_SEARCH_RANGE{2.0}; | |||
| const double MAX_DOWNSAMPLE_LINE_SEARCH_RANGE{144.0}; | |||
| const double MIN_DOWNSAMPLE_FACTOR_SCALE{0.3}; | |||
| const double MAX_DOWNSAMPLE_FACTOR_SCALE{3.0}; | |||
| const std::size_t MAX_NUMBER_FOLDS{5}; | |||
| // This isn't a hard limit be we increase the number of default training folds | |||
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I think, something wrong with this sentence.
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| // So how does the following work: we'd like "c * f * # rows" training rows. | ||
| // For k folds we'll have "(1 - 1 / k) * # rows" training rows. So we want | ||
| // to find the smallest integer k s.t. c * f * # rows <= (1 - 1 / k) * # rows. | ||
| // This gives k = ceil(1 / (1 - c * f)). However, we also upper bound this | ||
| // by MAX_NUMBER_FOLDS. |
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This is a very nice explanation!
tveasey
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Jan 13, 2020
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This makes two changes: