FeatureLabelCorrelation#
- class FeatureLabelCorrelation[source]#
Return the PPS (Predictive Power Score) of all features in relation to the label.
The PPS represents the ability of a feature to single-handedly predict another feature or 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.
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.predictors function
- n_top_featuresint , default: 5
Number of features to show, sorted by the magnitude of difference in PPS
- n_samplesint , default: 100_000
number of samples to use for this check.
- random_stateint , default: None
Random state for the ppscore.predictors function
- __init__(ppscore_params: Optional[Dict[Any, Any]] = None, n_top_features: int = 5, n_samples: int = 100000, random_state: Optional[int] = None, **kwargs)[source]#
- __new__(*args, **kwargs)#
Methods
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Add new condition function to the check. |
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Add condition that will check that pps of the specified feature(s) is less than the threshold. |
Remove all conditions from this check instance. |
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Run conditions on given result. |
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Return check configuration (conditions' configuration not yet supported). |
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Return check object from a CheckConfig object. |
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Deserialize check instance from JSON string. |
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Return check metadata. |
Name of class in split camel case. |
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Return parameters to show when printing the check. |
Remove given condition by index. |
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Run check. |
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Run check. |
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Serialize check instance to JSON string. |
