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_top_featuresint , default: 5
Number of features to show, sorted by the magnitude of difference in PPS
- random_stateint , default: None
Random state for the ppscore.predictors function
- min_pps_to_show: float, default 0.05
Minimum PPS to show a class in the graph
- __init__(ppscore_params=None, n_top_features: int = 5, random_state: Optional[int] = None, min_pps_to_show: float = 0.05, **kwargs)[source]#
- __new__(*args, **kwargs)#
Methods
Add new condition function to the check. |
|
|
Add new condition. |
|
Add new condition. |
Remove all conditions from this check instance. |
|
|
Run conditions on given result. |
|
Finalize the check result by adding the check instance and processing the conditions. |
Return check metadata. |
|
Name of class in split camel case. |
|
Return parameters to show when printing the check. |
|
Remove given condition by index. |
|
Run check. |
|
Run check. |
