# On Validating, Repairing and Refining Heuristic ML Explanations

## TL;DR

This paper evaluates the quality of heuristic explanations for ML models, especially boosted trees, revealing their inadequacies compared to rigorous methods across various datasets.

## Contribution

It extends previous rigorous explanation methods to boosted trees and assesses heuristic explanation quality, highlighting their limitations.

## Key findings

- Heuristic explanations are often inadequate for entire instance spaces.
- Rigorous explanations provide more reliable insights.
- Heuristic methods may mislead in model interpretation.

## Abstract

Recent years have witnessed a fast-growing interest in computing explanations for Machine Learning (ML) models predictions. For non-interpretable ML models, the most commonly used approaches for computing explanations are heuristic in nature. In contrast, recent work proposed rigorous approaches for computing explanations, which hold for a given ML model and prediction over the entire instance space. This paper extends earlier work to the case of boosted trees and assesses the quality of explanations obtained with state-of-the-art heuristic approaches. On most of the datasets considered, and for the vast majority of instances, the explanations obtained with heuristic approaches are shown to be inadequate when the entire instance space is (implicitly) considered.