Evaluating Explainers via Perturbation

Due to high complexity of many modern machine learning models such as deep convolutional networks, understanding the cause of model's prediction is critical. Many explainers have been designed to give us more insights on the decision of complex classifiers. However, there is no common ground on evaluating the quality of different classification methods. Motivated by the needs for comprehensive evaluation, we introduce the c-Eval metric and the corresponding framework to quantify the explainer's quality on feature-based explainers of machine learning image classifiers. Given a prediction and the corresponding explanation on that prediction, c-Eval is the minimum-power perturbation that successfully alters the prediction while keeping the explanation's features unchanged. We also provide theoretical analysis linking the proposed parameter with the portion of predicted object covered by the explanation. Using a heuristic approach, we introduce the c-Eval plot, which not only displays a strong connection between c-Eval and explainers' quality, but also serves as a low-complexity approach of assessing explainers. We finally conduct extensive experiments of explainers on three different datasets in order to support the adoption of c-Eval in evaluating explainers' performance.