ID3 Algorithm
The ID3 Algorithm (Iterative Dichotomiser 3) is a decision tree learning method used for classification tasks in machine learning. It works by selecting the attribute that best separates the data into distinct classes based on a measure called information gain. The algorithm recursively splits the dataset into subsets, creating branches in the decision tree until all data points are classified or a stopping criterion is met.
ID3 uses a top-down approach, starting with the entire dataset and progressively narrowing down the choices. It is particularly effective for categorical data and helps in visualizing decision-making processes. However, it can be prone to overfitting, especially with noisy data.