# Find-S Summary

**machine learning**:

- Math/Machine Learning Terminology
- Rule Post Pruning Summary
- Find-S Summary
- Candidate Elimination Summary
- Decision Trees Summary
- Stochastic Gradient Descent Summary
- Delta Training Rule Summary
- Perceptron Training Rule Summary
- A Summary Of Multilayer Neural Networks
- Abdullah's Machine Learning Notes

Before we start, remember that in the field of machine learning, **hypothesis** and **function** are synonyms.

Find-Sis a machine learning algorithm to find aboolean functionthat matches your training data.

The algorithm is very simple:

- Start with an initial hypothesis.
- Make sure your initial hypothesis is as specific as possible (i.e. all your training examples are predicted as negative by your hypothesis).

- For each positive training example in your training examples:
- If it is predicted as negative by your hypothesis:
- Generalize your hypothesis
*just enough*so that the training example is now predicted as positive.

- Generalize your hypothesis

- If it is predicted as negative by your hypothesis:

That’s it!

How do you generalize your hypothesis “just enough”? Find which attributes of your hypothesis disagree with the training example, and generalize just those attributes.

Some notes on Find-S:

- Is a
*concept learning*algorithm (in other words, can only find boolean functions). - Your data’s features must be discreet (i.e. not continuous).
- Does extremely bad with noisy training data
- A
*single*bad (i.e. misclassified) training example can lead to a severely poor performing hypothesis. - Doesn’t handle missing attributes.

- A
- Restriction biased. I.e. it only searches the hypothesis that can be represented by your chosen hypothesis representation. I.e. only contains the hypothesis that can be represented with your chosen hypothesis representation.