Arguably, the most important safeguard in building predictive models is complexity regularization to avoid overfitting the data. When models are overfit, their accuracy is lower on new data that wasn’t seen during training, and therefore when these models are deployed, they will disappoint, sometimes even leading decision makers to believe that predictive modeling “doesn’t work”. Overfit, however, is thankfully a well-known problem and every algorithm has ways to avoid it. CART® and C5 trees use pruning to remove branches that are prone to overfitting, CHAID trees require splits are statistically significant to add complexity to the trees. Neural networks use
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