Comparing Feature Importance and Rule Extraction for Interpretability on Text Data
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arXiv
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866911222099607552 |
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| author | Lopardo, Gianluigi Garreau, Damien |
| author_facet | Lopardo, Gianluigi Garreau, Damien |
| contents | Complex machine learning algorithms are used more and more often in critical tasks involving text data, leading to the development of interpretability methods. Among local methods, two families have emerged: those computing importance scores for each feature and those extracting simple logical rules. In this paper we show that using different methods can lead to unexpectedly different explanations, even when applied to simple models for which we would expect qualitative coincidence. To quantify this effect, we propose a new approach to compare explanations produced by different methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2207_01420 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Comparing Feature Importance and Rule Extraction for Interpretability on Text Data Lopardo, Gianluigi Garreau, Damien Machine Learning Artificial Intelligence Computation and Language Complex machine learning algorithms are used more and more often in critical tasks involving text data, leading to the development of interpretability methods. Among local methods, two families have emerged: those computing importance scores for each feature and those extracting simple logical rules. In this paper we show that using different methods can lead to unexpectedly different explanations, even when applied to simple models for which we would expect qualitative coincidence. To quantify this effect, we propose a new approach to compare explanations produced by different methods. |
| title | Comparing Feature Importance and Rule Extraction for Interpretability on Text Data |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2207.01420 |