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| Format: | Preprint |
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2026
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| Online Access: | https://arxiv.org/abs/2602.00179 |
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| _version_ | 1866915885541752832 |
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| author | Breeden, Joseph L. |
| author_facet | Breeden, Joseph L. |
| contents | For applications of machine learning in critical decisions, explainability is a primary concern, and often a regulatory requirement. Local linear methods for generating explanations, such as LIME and SHAP, have been criticized for being unstable near decision boundaries. In this paper, we explain that such concerns reflect a misunderstanding of the problem. The forecast uncertainty is high at decision boundaries, so consequently, the explanatory instability is high. The correct approach is to change the sequence of events and questions being asked. Nonlinear models can be highly predictive in some regions while having little or no predictability in others. Therefore, the first question is whether a usable forecast exists. When there is a forecast with low enough uncertainty to be useful, an explanation can be sought via a local linear approximation. In such cases, the explanatory instability is correspondingly low. When no usable forecast exists, the decision must fall to a simpler overall model such as traditional logistic regression. Additionally, these results show that some methods that purport to be explainable everywhere, such as ReLU networks or any piecewise linear model, have only an illusory explainability, because the forecast uncertainty at the segment boundaries is too high to be useful. Explaining an unusable forecast is pointless. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_00179 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | How Understanding Forecast Uncertainty Resolves the Explainability Problem in Machine Learning Models Breeden, Joseph L. Machine Learning For applications of machine learning in critical decisions, explainability is a primary concern, and often a regulatory requirement. Local linear methods for generating explanations, such as LIME and SHAP, have been criticized for being unstable near decision boundaries. In this paper, we explain that such concerns reflect a misunderstanding of the problem. The forecast uncertainty is high at decision boundaries, so consequently, the explanatory instability is high. The correct approach is to change the sequence of events and questions being asked. Nonlinear models can be highly predictive in some regions while having little or no predictability in others. Therefore, the first question is whether a usable forecast exists. When there is a forecast with low enough uncertainty to be useful, an explanation can be sought via a local linear approximation. In such cases, the explanatory instability is correspondingly low. When no usable forecast exists, the decision must fall to a simpler overall model such as traditional logistic regression. Additionally, these results show that some methods that purport to be explainable everywhere, such as ReLU networks or any piecewise linear model, have only an illusory explainability, because the forecast uncertainty at the segment boundaries is too high to be useful. Explaining an unusable forecast is pointless. |
| title | How Understanding Forecast Uncertainty Resolves the Explainability Problem in Machine Learning Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2602.00179 |