Re: doubt in lecture 11, deterministic noise
In general a more complex H implies lower "deterministic noise" but it is important to take into consideration the amount of training data that you have (N) when discussing Eout. In the example shown in lecture 11 the target function was very complex (50th order) and the training data was noiseless. We could see that a simple hypothesis (second order pol) gave a much better Eout than the more complex hypothesis (10th order polynomial). In this case there was only "deterministic noise" and the more complex Hypothesis performed much worse even if the "deterministic noise" was lower for the more complex H.
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