## 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. Initilizehto the most specific hypothesis inH2. For each positive training instancexFor each attribute contraintain_{i}hIf the contraintais satisfied by_{i}xthen do nothing Else replaceain_{i}hby the next more general constraint that is satisfied byx3. Output the hypothesish

## 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

**consistent with the data (i.e., the correct target concept), or whether there are many other consistent hypotheses as well.**

*H*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

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