We address the problem of finding influential training samples for a particular case of tree ensemble-based models, e.g., Random Forest (RF) or Gradient Boosted Decision Trees (GBDT). A natural way of formalizing this problem is studying how the model's predictions change upon leave-one-out retraining, leaving out each individual training sample. Recent work has shown that, for parametric models, this analysis can be conducted in a computationally efficient way. We propose several ways of extending this framework to non-parametric GBDT ensembles under the assumption that tree structures remain fixed. Furthermore, we introduce a general scheme of obtaining further approximations to our method that balance the trade-off between performance and computational complexity. We evaluate our approaches on various experimental setups and use-case scenarios and demonstrate both the quality of our approach to finding influential training samples in comparison to the baselines and its computational efficiency.
Boris Sharchilev (Yandex)
Yury Ustinovskiy (Princeton University)
Pavel Serdyukov (Yandex)
Maarten de Rijke (University of Amsterdam)
Related Events (a corresponding poster, oral, or spotlight)
2018 Oral: Finding Influential Training Samples for Gradient Boosted Decision Trees »
Thu Jul 12th 12:10 -- 12:20 PM Room A6