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Poster
Finding Influential Training Samples for Gradient Boosted Decision Trees
Boris Sharchilev · Yury Ustinovskiy · Pavel Serdyukov · Maarten de Rijke

Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #223
in Posters Thu »

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.

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Author Information

Boris Sharchilev (Yandex)
Yury Ustinovskiy (Princeton University)
Pavel Serdyukov (Yandex)
Maarten de Rijke (University of Amsterdam)

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