Which data science model is best for explainability for prediction problems?
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 &quot;black-box&quot;
Like:

SHAP Values
Partial Dependence plot
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 &quot;correct&quot; conclusions when explaining it.