Implementation of the Feature Tweaking algorithm for XGBoost models
Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking, Tolomei et. al. https://arxiv.org/abs/1706.06691
The package may be installed by
pip installl git+https://github.com/nicholaskarlsen/feature-tweaking
# the feature-tweaking class may be instantiated in the following way
ft = FeatureTweaking(model, continuous_features, categorical_features, continuous_metric="l1", categorical_metric="kronecker delta")
# which may then be used to generate counterfactual examples
counterfactuals = ft.generate_counterfactuals(X.iloc[i], epsilon=0.1)
# the factual class is inferred from the passed model and a pandas dataframe contiaining the set of epsilon-satisfactory examples
# is returned along with their distance from the factual example measured using the selected metric
Parameter | Description |
---|---|
`model (xgboost.Booster | xgboost.sklearn.XGBModel)` |
continuous_features (list<str>) |
list containing column names of the continuous features to tweak |
categorical_features(list<str>) |
list containing column names of the categorical features to tweak |
continuous_metric (str) |
metric to use for all of the continuous features. see FeatureTweaking._metrics for a list of currently implemented metrics |
categorical_metric (str) |
metric to use for all of the categorical features. see FeatureTweaking._categorical_metrics for a list of currently implemented metrics |