[wptelegram-join-channel]

## FIND-S Algorithm Unanswered Questions in Machine Learning

FIND S Algorithm is used to find the Maximally Specific Hypothesis. Using the Find-S algorithm gives a single maximally specific hypothesis for the given set of training examples.

### Find-S Algorithm Machine Learning

```1. Initilize h to the most specific hypothesis in H
2. For each positive training instance x
For each attribute contraint ai in h
If the contraint ai is satisfied by x
then do nothing
Else
replace ai in h by the next more general constraint that is satisfied by x
3. Output the hypothesis h```

## Video Tutorial

### Unanswered Questions by Find-S algorithm in Machine Learning

1. Has the learner converged to the correct target concept? Although FIND-S will find a hypothesis consistent with the training data, it has no way to determine whether it has found the only hypothesis in H consistent with the data (i.e., the correct target concept), or whether there are many other consistent hypotheses as well.

2. Why prefer the most specific hypothesis? In case there are multiple hypotheses consistent with the training examples, FIND-S will find the most specific. It is unclear whether we should prefer this hypothesis over the most general or some other hypothesis of intermediate generality.

3. Are the training examples consistent? In most practical learning problems there is some chance that the training examples will contain at least some errors or noise. Such inconsistent sets of training examples can severely mislead FIND-S, given the fact that it ignores negative examples. We would prefer an algorithm that could at least detect when the training data is inconsistent and, preferably, accommodate such errors.

4. What if there are several maximally specific consistent hypotheses? In the hypothesis language H for the EnjoySport task, there is always a unique, most specific hypothesis consistent with any set of positive examples. However, for other hypothesis spaces there can be several maximally specific hypotheses consistent with the data.

Summary