**Introduction : **

Find-S algorithm is a basic concept learning algorithm in machine learning. 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. 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, 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 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 used 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 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 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 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, the 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

## Recommended Posts:

- Different Types of Clustering Algorithm
- Asynchronous Advantage Actor Critic (A3C) algorithm
- Affinity Propagation in ML | To find the number of clusters
- Facebook News Feed Algorithm
- Gradient Descent algorithm and its variants
- k-nearest neighbor algorithm in Python
- ML | T-distributed Stochastic Neighbor Embedding (t-SNE) Algorithm
- ML | Mini Batch K-means clustering algorithm
- ML | Expectation-Maximization Algorithm
- ML | Reinforcement Learning Algorithm : Python Implementation using Q-learning
- Genetic Algorithm for Reinforcement Learning : Python implementation
- Silhouette Algorithm to determine the optimal value of k
- Implementing DBSCAN algorithm using Sklearn
- ML | ECLAT Algorithm
- Implementing Apriori algorithm in Python
- Encoding Methods in Genetic Algorithm
- Explanation of Fundamental Functions involved in A3C algorithm
- Crossover in Genetic Algorithm
- ML | Handling Imbalanced Data with SMOTE and Near Miss Algorithm in Python
- ML | Frequent Pattern Growth Algorithm

If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.

Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.