SingleFeatureContributionTrainTest¶
- class SingleFeatureContributionTrainTest[source]¶
Return the Predictive Power Score of all features, in order to estimate each feature’s ability to predict the label.
The PPS represents the ability of a feature to single-handedly predict another feature or label. In this check, we specifically use it to assess the ability of each feature to predict the label. A high PPS (close to 1) can mean that this feature’s success in predicting the label is actually due to data leakage - meaning that the feature holds information that is based on the label to begin with.
When we compare train PPS to test PPS, A high difference can strongly indicate leakage, as a feature that was “powerful” in train but not in test can be explained by leakage in train that does not affect a new dataset.
Uses the ppscore package - for more info, see https://github.com/8080labs/ppscore
- Parameters
- ppscore_paramsdict , default: None
dictionary of additional parameters for the ppscore predictor function
- n_show_topint , default: 5
Number of features to show, sorted by the magnitude of difference in PPS
- __new__(*args, **kwargs)¶
Methods
Add new condition function to the check. |
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Add new condition. |
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Add new condition. |
Remove all conditions from this check instance. |
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Run conditions on given result. |
Name of class in split camel case. |
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Return parameters to show when printing the check. |
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Remove given condition by index. |
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Run check. |
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Run check. |
