Question

Discuss necessary measure required to select the attributes for building a decision tree using id3 algorithm.

Answer 1

Attribute Selection Gain measures how well a given attribute separates training examples into targeted classes. The one with the highest information (information being the most useful for classification) is selected. In order to define gain, we first borrow an idea from information theory called entropy.

Answer 2

Answer:

Iterative Dichotomiser 3, or D3, is a classification algorithm that adopts a greedy method of decision tree construction.

Explanation:

An ID3 algorithm is what?

It is a classification method that adopts a greedy strategy by choosing the optimal characteristic that produces the highest Information Gain (IG) or the lowest Entropy (H).

Following are the steps in the ID3 algorithm:

1. Determine the dataset's entropy
2. For each trait or property
3. For each of its category values, calculate the entropy
4. Determine the feature's information gain
5. Find the feature that will provide the most information
6. Continue doing it until we find the right tree

Although it frequently creates tiny trees, this method does not always succeed in doing so.