Practice question · Multiple choice
A model fits its training data almost perfectly and performs poorly on new data. Which fitting concept describes this, and what is the geometric picture?
Hints
- Recall what a degree-19 polynomial through 20 noisy points looks like between the points.
- Ask what the model learned that it should not have.
Show the answer
A. Overfitting, the model passes through the noise as well as the signal
Why
The model fitted the noise as faithfully as the signal, and noise does not repeat in new data. It is the interpolation-versus-fitting distinction under another name, which is why held-out validation exists, and why a model flexible enough to fit anything has told you nothing by fitting your data.
Practise The Definite Integral and Area
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