Overfitting & Variable Selection & Stepwise Regression

Описание к видео Overfitting & Variable Selection & Stepwise Regression

Parsimony is important. If your model is too big and complex, it may overfit the training data. That means it performs well on the training data but performs poorly on the test data. Then you should simplify the model and eliminate some variables.

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