**Introduction : **

The find-S algorithm is a basic concept learning algorithm in machine learning. The find-S algorithm finds the most specific hypothesis that fits all the positive examples. We have to note here that the algorithm considers only those positive training example. The find-S algorithm starts with the most specific hypothesis and generalizes this hypothesis each time it fails to classify an observed positive training data. Hence, the Find-S algorithm moves from the most specific hypothesis to the most general hypothesis.

**Important Representation : **

**?**indicates that any value is acceptable for the attribute.- specify a single required value ( e.g., Cold ) for the attribute.
**ϕ**indicates that no value is acceptable.- The most
**general hypothesis**is represented by:**{?, ?, ?, ?, ?, ?}** - The most
**specific hypothesis**is represented by:**{ϕ, ϕ, ϕ, ϕ, ϕ, ϕ}**

**Steps Involved In Find-S : **

- Start with the most specific hypothesis.
**h = {ϕ, ϕ, ϕ, ϕ, ϕ, ϕ}** - Take the next example and if it is negative, then no changes occur to the hypothesis.
- If the example is positive and we find that our initial hypothesis is too specific then we update our current hypothesis to a general condition.
- Keep repeating the above steps till all the training examples are complete.
- After we have completed all the training examples we will have the final hypothesis when can use to classify the new examples.

**Example : **

Consider the following data set having the data about which particular seeds are poisonous.

First, we consider the hypothesis to be a more specific hypothesis. Hence, our hypothesis would be : **h = {ϕ, ϕ, ϕ, ϕ, ϕ, ϕ}**

**Consider example 1 :**

The data in example 1 is { GREEN, HARD, NO, WRINKLED }. We see that our initial hypothesis is more specific and we have to generalize it for this example. Hence, the hypothesis becomes : **h = { GREEN, HARD, NO, WRINKLED }**

**Consider example 2 : **

Here we see that this example has a negative outcome. Hence we neglect this example and our hypothesis remains the same. **h = { GREEN, HARD, NO, WRINKLED }**

**Consider example 3 : **

Here we see that this example has a negative outcome. Hence we neglect this example and our hypothesis remains the same. **h = { GREEN, HARD, NO, WRINKLED }**

**Consider example 4 : **

The data present in example 4 is { ORANGE, HARD, NO, WRINKLED }. We compare every single attribute with the initial data and if any mismatch is found we replace that particular attribute with a general case ( ” ? ” ). After doing the process the hypothesis becomes : **h = { ?, HARD, NO, WRINKLED }**

**Consider example 5 : **

The data present in example 5 is { GREEN, SOFT, YES, SMOOTH }. We compare every single attribute with the initial data and if any mismatch is found we replace that particular attribute with a general case ( ” ? ” ). After doing the process the hypothesis becomes : **h = { ?, ?, ?, ? }**

Since we have reached a point where all the attributes in our hypothesis have the general condition, example 6 and example 7 would result in the same hypothesizes with all general attributes. **h = { ?, ?, ?, ? }**

Hence, for the given data the final hypothesis would be : **Final Hyposthesis: h = { ?, ?, ?, ? }**

**Algorithm : **

1. Initialize h to the most specific hypothesis in H 2. For each positive training instance x For each attribute constraint a, in h If the constraint a, is satisfied by x Then do nothing Else replace a, in h by the next more general constraint that is satisfied by x 3. Output hypothesis h