Overfitting occurs when a model memorizes the training data too...

We take a closer look at Overfitting occurs when a model memorizes the training data too... and its implications for both beginners and experts in model.

Overfitting occurs when a model memorizes the training data too precisely, including its noise and randomness, instead of learning the general underlying pattern.
Imagine fitting curves to data points that roughly follow a parabolic shape.
A linear model is too basic. Since it can’t capture the curve, it underfits, resulting in high error on both training and test data.
A quadratic model reflects the true structure of the data, producing low training and test error - this is the right balance.
But if you move to a cubic model, it may warp itself to pass exactly through every training point. This gives extremely low training error, but it fails to generalize. On new data, the predictions swing too much, causing high test error.
That behavior is overfitting: great performance on data the model has seen, poor performance on data it hasn’t.
C: Welch Labs

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