Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking

@inproceedings{Tolomei2017InterpretablePO,
  title={Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking},
  author={Gabriele Tolomei and Fabrizio Silvestri and Andrew Haines and Mounia Lalmas},
  booktitle={KDD},
  year={2017}
}
Machine-learned models are often described as "black boxes". In many real-world applications however, models may have to sacrifice predictive power in favour of human-interpretability. When this is the case, feature engineering becomes a crucial task, which requires significant and time-consuming human effort. Whilst some features are inherently static, representing properties that cannot be influenced (e.g., the age of an individual), others capture characteristics that could be adjusted (e.g… CONTINUE READING

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