Nice post. Nice that you show the train-test split. Regarding the Shapley, it's probably better to look at the coefficients directly than estimate them via Shapley values.
Not sure my understanding is correct, maybe you can shed some light. In a linear model, a higher coefficient for a feature, the more a feature played a role in making a prediction. However, when variables in a regression model are correlated, these conclusions don't hold anymore.
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Nice post. Nice that you show the train-test split. Regarding the Shapley, it's probably better to look at the coefficients directly than estimate them via Shapley values.
forem.julialang.org/rikhuijzer/ran...
That's a model without coefficients
Not sure my understanding is correct, maybe you can shed some light. In a linear model, a higher coefficient for a feature, the more a feature played a role in making a prediction. However, when variables in a regression model are correlated, these conclusions don't hold anymore.