Machine Learning: Tree-Based Models and Ensemble Learning

Introduction

What this guide covers

Introduction // Machine Learning · Advanced Statistical Approach

Overview

Topics covered

• Introduction
• Tree-Based Models and Ensemble Learning
• Theoretical Foundations and Tree Induction
• Mathematical Division Criteria
• Classification: Gini Impurity and Entropy
• Regression: Variance Reduction

Related concepts

Main connections

• Decision trees, Random Forests & XGBoost
• Variance & standard deviation
• Supervised machine learning
• Mean & expected value
• Overfitting & cross-validation
• Model interpretability (SHAP / LIME)

Compatible notation

Mathematical sample in LaTeX

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