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It's not that accuracy will always be sacrificed if one wants an explainable model. The point is: if interpretability is an important constraint, it could preve

It's not that accuracy will always be sacrificed if one wants an explainable model. The point is: if interpretability is an important constraint, it could prevent improvements on accuracy. Sometimes, the best interpretable model is as good as a black box, and that's great. When this is not the case, the trade-off is that one should see what's more important for the actual problem. Perhaps interpretability is not a big deal. Another solution is to try to extract interpretability from the more accurate black box model with something like SHAP.

1_over_n 7y ago
This is a great point. There is a general lack of understanding about what it means for models to be interpretable & explainable. These words get thrown around often by people who don't understand the definition, and also the trade off with accuracy. Some papers i found interesting on the subject: https://arxiv.org/abs/1606.03490 https://arxiv.org/abs/1606.03490 https://arxiv.org/abs/1707.03886 https://arxiv.org/abs/1707.03886 https://arxiv.org/abs/1806.07552 https://arxiv.org/abs/1806.07552 https://arxiv.org/abs/1702.08608 https://arxiv.org/abs/1702.08608 (i found this was a good sumary of the issues)