Decision Trees Intuition

Decision trees are one of the most intuitive ML algorithms. They make predictions by asking a series of yes/no questions, just like a flowchart.

How Decision Trees Work

Building a Tree: Finding the Best Splits

Gini Impurity

Trees find the best splits using metrics like Gini Impurity.

Advantages of Decision Trees

Key Takeaways

• Decision trees split data with yes/no questions
• Each split aims to create pure groups (all same class)
• Gini impurity measures how mixed a node is
• Trees are interpretable - you can follow the logic
• Can overfit if too deep - need to control complexity
• Works for classification and regression

Next, we'll learn Random Forests - many trees working together!