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Imagine you have to create a model to explain to stakeholders e.g. to predict price, weight, sales etc.. Which regression models offer the best in terms of explainability and interprability? ... Which model is best for this?

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Personally I think linear (through model's coefficients/weights) and tree-based models (gain importance) are the best for explainability

But this is not restricted to those models since you can use model agnostic techniques to explaine any model, even those consider as "black-box"

Like:

  1. SHAP Values
  2. Partial Dependence plot
  3. LIME

You can check this good resource to learn more on that.

A final comment I'd say is that you cannot forget the fact that an essential part of model explainability is model performance, It does not make sense to have a model that is easily explainable but with a very low performance, since those structures found by the model do not generalize well. So a good model in performance will lead you to the "correct" conclusions when explaining it.

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  • $\begingroup$ agreed on that, but for linear regression alot of assumptons are made about underlying data, don't you need to standarize the data and remove correlated variables etc for these models? $\endgroup$
    – Maths12
    Feb 5 at 17:38
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    $\begingroup$ Every model has assumptions, in case of linear models, as you mention multicollinearity is something to take into account as it change completely the interpretation of the coefficients (This also affects PD Plots) I propose using PCA regression so that you guarantee that the is no correlation in your features $\endgroup$ Feb 5 at 17:48

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