MMMs are powerful models and that means they’re subject to what modelers call “over-fitting”. The idea is that you can build a model that fits really, really well to the data that the model is trained on, but that hasn’t found the actual underlying causal relationships in the data.Over-fitting happens when the model is “too powerful” and fits to noise in the data, instead of the signal. And if your model is too overfit, it will be fit only to noise and will miss out on the signal entirely.This article will cover the main reasons behind model overfitting, explore the misleading nature of in-sample R-squared, and touch on out-of-sample validation as a practical method to validate your media mix modeling. …