Decision Trees
Decision Trees are a type of artificial intelligence model used for classification and regression tasks. They work by splitting data into branches based on feature values, forming a tree-like structure where each internal node represents a decision based on an attribute, each branch represents an outcome, and each leaf node represents a final prediction. Decision Trees are intuitive, easy to visualize, and can handle both numerical and categorical data.
Decision Trees
Decision Trees are a type of artificial intelligence model used for classification and regression tasks. They work by splitting data into branches based on feature values, forming a tree-like structure where each internal node represents a decision based on an attribute, each branch represents an outcome, and each leaf node represents a final prediction. Decision Trees are intuitive, easy to visualize, and can handle both numerical and categorical data.
💡 Key Takeaways
- Understand what a decision tree is and how it predicts by splitting data on feature values.
- Learn common split criteria (Gini impurity and entropy) and how they guide decisions.
- Recognize overfitting risks and how pruning, max depth, or minimum samples can help.
- Learn to read a trained tree and interpret its decisions on new data.
❓ Frequently Asked Questions
What is a decision tree?
A simple predictive model that splits data based on feature questions, forming a tree where each internal node tests a feature, branches by outcome, and leaves give a prediction.
How does a decision tree decide where to split data?
Splits aim to reduce impurity and increase separation of outcomes. Common criteria include Gini impurity and entropy (information gain) for classification, or variance reduction for regression.
What is pruning and why is it useful?
Pruning removes branches that don’t improve predictive power, reducing overfitting and making the model simpler and more generalizable. Methods include cost-complexity pruning and reduced-error pruning.
What is the difference between classification and regression trees?
Classification trees predict discrete categories; regression trees predict continuous numeric values. They use similar split rules, but leaves store class labels versus numeric estimates.