Information Gain in Decision Trees:
1. Purpose of Decision Trees: Decision trees help make decisions by breaking down a problem into smaller, manageable steps, like a flowchart.
2. Entropy: Entropy is a measure of confusion or disorder. In decision trees, it gauges how mixed up our data is in terms of categories.
3. Information Gain: Information Gain is like a guide for decision trees. It helps decide which question (feature) to ask first to make our dataset less confusing.
4. How it Works: At each step, the tree looks at different questions (features) and picks the one that reduces confusion the most—this is high Information Gain.
5. Goal: The goal is to keep asking the best questions (features) to split our data until we reach clear and tidy groups.