# Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking

@article{Tolomei2017InterpretablePO,
title={Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking},
author={Gabriele Tolomei and Fabrizio Silvestri and Andrew Haines and Mounia Lalmas},
journal={Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
year={2017}
}
• Published 20 June 2017
• Computer Science
• Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
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…
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