Detect highly null columns #121
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angela97lin
changed the title
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## master #121 +/- ##
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+ Coverage 94.08% 94.13% +0.05%
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Lines 1436 1467 +31
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+ Hits 1351 1381 +30
- Misses 85 86 +1
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quick comment. if we drop columns on training data, we need to drop the same columns on the test data even if they aren't highly null. would it be better to create this a component in the pipeline with fit/predict? |
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Hmmm, since the columns are dropped before it is passed to _do_iteration(...) where the split into training and testing data happens, we don't have to manually drop the columns in the test data too? Still, do you think it'd make more sense with the restructuring of pipelines? |
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i agree that works during testing, but what about when we call the fitted pipeline on new data in the future. we'd need to make sure to drop the same columns |
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and actually, as I think about it, we don't want to drop columns based on the test data. that would be creating label leakage. we should only drop columns based on what is in the training data split e.g "learn" what columns to drop on the train data, but reuse that learned selection on test data and future usage. |
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Ah, I think I see what you mean. In that case, should I connect (rebase) this with the pipeline components PR we’re working on for pipeline_v2 to create an actual Component and then merge it when we merge in the new pipeline code? |
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ya, I think that would make sense |
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this PR should probably change to "Detect highly null columns" |
Adding preprocessing function to remove highly null columns and incorporating into Auto(*) classes
Fixes #116