Iterative Dichotomiser 3 (ID3)

ID3 is a classic algorithm used to build Decision Trees. It selects the best attribute at each node using the information gain criterion, leading to the creation of a tree that strives to efficiently represent and categorize the training dataset.

Notes:

• ID3 uses a top-down, greedy search approach to grow the decision tree by iteratively choosing the best attribute for each internal node based on information gain.
• It works well with categorical or discrete attributes, and it doesn't handle continuous attributes directly; preprocessing may be necessary.
• Because ID3 doesn't handle Missing Values, imputation or other strategies are often needed to accommodate datasets with missing data.
• The ID3 algorithm is sensitive to noisy data and Overfitting, so precautions such as pruning or using more advanced tree algorithms like C4.5 and C5.0 have been developed to address these limitations.