Decision Trees Unveiled: From ID3 to CART to Random Forests to XGBoost

Author(s): Joseph Robinson, Ph.D.

Originally published on Towards AI.

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Visual geneated by sample code provided in this blog tutorial.

· Introduction· Understanding Basic Decision Trees: ID3 Algorithm· CART Algorithm: Classification and Regression Trees· Advanced Decision Trees: From Bagging to Boosting· XGBoost: Extreme Gradient Boosting· Benchmarking the Algorithms· Pros and Cons of Each Method· Practical Considerations for Decision Trees in Production· Conclusion· Call to Action

Imagine a model miming how humans make decisions: starting with a broad question and gradually narrowing down the possibilities based on the answers until we arrive at a clear conclusion. This is the essence of a decision tree—one of today’s most intuitive and powerful machine learning algorithms. Decision trees lie at the heart of data-driven decision-making, whether determining if a patient is at risk for a specific disease or predicting customer churn.

A decision tree is a model that breaks down data into branches based on feature values, creating a flowchart-like structure where decisions are made at each node.

A decision tree is a step-by-step guide that asks questions about the data and splits it into increasingly homogeneous groups. Hence, we train a model that is easy to understand, visualize, and explain, making decision trees a popular choice… Read the full blog for free on Medium.

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Published via Towards AI