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How to handle S@98 estimation when building an honest forest #225

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adam2392 opened this issue Feb 20, 2024 · 1 comment

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adam2392 commented Feb 20, 2024

Rejection bootstrap sampling: if a bootstrap sample does not have enough control samples (e.g. 50 for S@98) to estimate S@98 properly, then reject this bootstrap sampled indices and repeat
Upweight the sample weights based on class: this is the strategy sklearn currently has
Stratify bootstrap sample:

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adam2392 commented Feb 20, 2024

My inclination is just do 1

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