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A Practical Guide to Machine Learning with Python

Introduction to Tree-Based Methods

Explore the fundamentals of tree-based machine learning methods such as decision trees, random forests, and gradient boosting. Understand how these techniques split data, reduce overfitting, and improve prediction accuracy for both numeric and categorical outcomes.

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Quick Overview

Tree-based learning algorithms, also known as CARTClassification and Regression Trees, are popular for predicting numeric and categorical outputs.

Tree-based methods (decision trees, bagging, random forests, boosting, etc.) are highly effective for supervised learning. This is partly due to their high accuracy and versatility, as they can ...