How to implement Decision Trees from scratch with Python

TL;DR

Learn how to build decision trees from scratch, exploring features, nodes, branches, and leaf nodes.

Transcript

hey and welcome in this lesson we're going to learn how to implement decision trees from scratch so let's first take a look at how decision trees work let's say this is a data set that we have it's a data set of whether we can afford a house or not and it has information of which neighborhood it is in and how many rooms it has and also the affordab... Read More

Key Insights

• 🌲 Decision trees split data based on features to create branches and leaf nodes for classification.
• 💁 Information gain measures the reduction in entropy through splitting based on specific features.
• ✋ Stopping criteria like max depth and minimum impurity decrease control tree growth to prevent overfitting.
• 🌲 Implementing decision trees involves creating root nodes, child nodes, and recursively growing the tree.

Questions & Answers

Q: How do decision trees work?

Decision trees split data based on features like neighborhood or room count, creating branches and leaf nodes to classify outcomes.

Q: What is information gain in building decision trees?

Information gain quantifies the reduction in entropy by splitting data based on a particular feature, helping decide the best split.

Q: What are the stopping criteria for decision tree growth?

Stopping criteria include max depth, minimum samples per split, and minimum impurity decrease to control tree growth and avoid overfitting.

Q: How are decision trees implemented from scratch?

Decision trees are built recursively, considering information gain, entropy, and feature thresholds to create nodes and grow the tree step by step.

Summary & Key Takeaways

• Decision trees work by splitting data based on features like neighborhood or room count.

• Trees are built through information gain, entropy calculations, and stopping criteria.

• Implementation involves creating root nodes, child nodes, and recursively growing the tree.