Decision Tree in Machine Learning | Classification & Regression | AI & ML – Chapter 38 скачать в хорошем качестве

Decision Tree in Machine Learning | Classification & Regression | AI & ML – Chapter 38

Welcome to AI & ML – Chapter 38! In this session, we’ll explore Decision Trees, one of the most widely used algorithms in supervised learning for both classification and regression tasks. You’ll learn: What is a Decision Tree? Structure: root node, branches, and leaf nodes Splitting criteria (Gini Index, Entropy, Information Gain) Difference between classification and regression trees Advantages and limitations of decision trees Real-world applications (finance, healthcare, business analytics, etc.) Practical coding examples for building decision trees in ML This tutorial is brought to you by TRIARIGHT, your trusted training institute for Artificial Intelligence & Machine Learning. By the end of this lesson, you’ll understand how to build, train, and evaluate decision tree models for predictive tasks.