Instance-based Explanations for Gradient Boosting Machine Predictions with AXIL Weights

We show that regression predictions from linear and tree-based models can be represented as linear combinations of target instances in the training data. This also holds for models constructed as ensembles of trees, including Random Forests and Gradient Boosting Machines. The weights used in these linear combinations are measures of instance importance, complementing existing measures of feature importance, such as SHAP and LIME. We refer to these measures as AXIL weights (Additive eXplanations with Instance Loadings). Since AXIL weights are additive across instances, they offer both local and global explanations. Our work contributes to the broader effort to make machine learning predictions more interpretable and explainable.

Improved Text Classification via Test-Time Augmentation

Test-time augmentation -- the aggregation of predictions across transformed examples of test inputs -- is an established technique to improve the performance of image classification models. Importantly, TTA can be used to improve model performance post-hoc, without additional training. Although test-time augmentation (TTA) can be applied to any data modality, it has seen limited adoption in NLP due in part to the difficulty of identifying label-preserving transformations. In this paper, we present augmentation policies that yield significant accuracy improvements with language models. A key finding is that augmentation policy design -- for instance, the number of samples generated from a single, non-deterministic augmentation -- has a considerable impact on the benefit of TTA. Experiments across a binary classification task and dataset show that test-time augmentation can deliver consistent improvements over current state-of-the-art approaches.