de Rijke, M.

Serdyukov, P.

Sharchilev, B.

Ustinovsky, Y.

English

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.</p

International Migration, Integration and Social Cohesion online publications

Finding Influential Training Samples for Gradient Boosted Decision Trees

https://pure.uva.nl/ws/files/62551338/sharchilev18a.pdf

Finding Influential Training Samples for Gradient Boosted Decision Trees

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International Migration, Integration and Social Cohesion online publications