Decision Tree Regression Explained | Intuition, Splits & Predictions скачать в хорошем качестве

Decision Tree Regression Explained | Intuition, Splits & Predictions

Decision Tree Regression is a powerful machine learning model that predicts values by splitting data into decision-based regions. In this video, we explain Decision Tree Regression from scratch, focusing on intuition and visual understanding instead of complex math. In this video, you’ll learn: What Decision Tree Regression is How a decision tree makes predictions step by step What splits, nodes, and leaves represent How the model chooses the best split Why Decision Trees can model non-linear patterns The problem of overfitting in Decision Trees When Decision Tree Regression works best We walk through how data is repeatedly split based on feature values and how predictions are made using the average value in leaf nodes. This makes Decision Tree Regression easy to understand and extremely interpretable. This video is ideal for: Machine Learning beginners Students learning tree-based models Anyone moving beyond Linear and Polynomial Regression Developers building intuition before ensemble models 📌 Topics Covered: Decision Tree Regression basics Tree structure (root, nodes, leaves) Splitting logic Overfitting intuition Model strengths and limitations Mastering Decision Tree Regression sets the foundation for advanced models like Random Forests and Gradient Boosting. If this helped, like, share, and subscribe for more ML concepts explained simply 🚀🌳