### Jethro's Braindump

Training data can mislead the learner, and result in overfitting. To overcome this problem, we can restrict the search space to some hypothesis space $$\mathcal{H}$$. This can be seen as introducing prior knowledge to the learning task. Is such prior knowledge necessary?

## The No-Free-Lunch Theorem

The No-Free-Lunch theorem states that for binary classification tasks, for every learner there exists a distribution on which it fails. We say that the learner fails if, upon receiving i.i.d. examples from that distribution, its output hypothesis is likely to have a large risk, whereas for the same distribution, there exists another learner that will output a hypothesis with a small risk. In other words, every learner has tasks on which it fails while others succeed.

Therefore, when approaching a particular learning problem, defined by some distribution $$D$$, we should have some prior knowledge on $$D$$. One type of such prior knowledge is that $$D$$ comes from some specific parametric family of distributions. Another type of such prior knowledge is that there exists $$h$$ in some predefined hypothesis class $$H$$, such that $$L_D(h) = 0$$.

## Error Decomposition

we can decompose the error of an $$ERM_H$$ predictor into two components as follows. Let $$h_S$$ be an $$ERM_H$$ hypothesis. Then we can write:

$$L_D(h_S) = \epsilon_{\textrm{app}} + \epsilon_{\textrm{est}}$$

where $$\epsilon_{\textrm{app}} = \textrm{min}_{h\in H} L_D(h)=$$, and $$\epsilon_{\textrm{est}} = L_D(h_S) - \epsilon_{\textrm{app}}$$.

The approximation error is the minimum risk achievable by a predictor in the hypothesis class. This term measures how much risk we have because we restrict ourselves to a specific class (how much inductive bias we have). This approximation error does not depend on the sample size, and is solely determined by the hypothesis class chosen.

Under the realizability assumption, the approximate error is zero. In the agnostic case, the approximation error can be large (it always includes the error of the Bayes optimal predictor).

The estimation error is the difference between the approximation error and the error achieved by the ERM predictor. The estimation error results because the empirical risk (training error) is only an estimate of the true risk.

Since our goal is to minimize the total error, we face a tradeoff, called the bias-complexity tradeoff. Choosing $$H$$ to be a very rich class decreases the approximation error, but may increase the estimation error, as a rich $$H$$ might lead to overfitting. Learning theory studies how rich we can make $$H$$ while still maintaining reasonable estimation error.