Data Health: Uniqueness#1785
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@@ Coverage Diff @@
## main #1785 +/- ##
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+ Coverage 100.0% 100.0% +0.1%
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Files 250 252 +2
Lines 19972 20047 +75
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+ Hits 19964 20039 +75
Misses 8 8
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chukarsten
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ParthivNaresh,
angela97lin,
bchen1116,
dsherry,
freddyaboulton and
jeremyliweishih
angela97lin
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Left a few comments, but LGTM!
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Cool stuff, love the examples! 🚀
| from .class_imbalance_data_check import ClassImbalanceDataCheck | ||
| from .high_variance_cv_data_check import HighVarianceCVDataCheck | ||
| from .multicollinearity_data_check import MulticollinearityDataCheck | ||
| from .uniqueness_data_check import UniquenessDataCheck |
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Wonderful, let's add this to the API ref too! 😁
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| res = X.apply(UniquenessDataCheck.uniqueness_score) | ||
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| if is_regression(self.problem_type): |
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Interesting, what happens in the case of a binary classification problem? Is nothing flagged? :o Maybe we should make explicit that this is only used for multiclass/regression problems (rather than classification)?
| "import pandas as pd\n", | ||
| "from evalml.data_checks import UniquenessDataCheck\n", | ||
| "\n", | ||
| "X = pd.DataFrame({'most_unique': [float(x) for x in range(10)], # [0,1,2,3,4,5,6,7,8,9]\n", |
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Nice, super clear example!!
angela97lin
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Feb 9, 2021
| "errors": [] | ||
| } | ||
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| data = pd.DataFrame({'multiclass_too_unique': ["Cats", "Are", "Absolutely", "The", "Best"] * 20, |
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addresses #1744