# Contextual Local Explanation for Black Box Classifiers

Source: [Contextual Local Explanation for Black Box Classifiers](<https://www.sota2.com/research/paper/68fa66741e209131cea1032c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T05:59:47.071Z

Authors: Zijian Zhang, Fan Yang, Haofan Wang, Xia Hu

Published: 2019-10-02T03:58:12.000Z

## Abstract

We introduce a new model-agnostic explanation technique which explains the prediction of any classifier called CLE. CLE gives an faithful and interpretable explanation to the prediction, by approximating the model locally using an interpretable model. We demonstrate the flexibility of CLE by explaining different models for text, tabular and image classification, and the fidelity of it by doing simulated user experiments.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1910.00768>)

[PDF](<https://arxiv.org/pdf/1910.00768.pdf>)