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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: SHAP Values Partial Dependence plot LIME You can check this ...


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If the sum of the two feature makes sense on the domain semantically, it might be a good idea. But while trees can handle redundant features pretty well, increasing the number of features without adding any extra "value" or "information" can lead to lower performance in certain situations. For example, if there is no added value and you ...


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This approach makes some sense but it's not the best approach for several reasons. First, this control variable might not always be last in importance because it's possible that some other variable also don't have any impact at all on the target variable (outcome). More importantly, the concept of a control group/control variable is useful in cases where one ...


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If you want to make an inference on an order where a categorical variable was not seen in the training data, you could train the model on a hash bucket representation of that variable. If using tensorflow, you can leverage: https://www.tensorflow.org/api_docs/python/tf/feature_column/categorical_column_with_hash_bucket Or implement yourself.


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