Decision Trees & Random Forests: The Best ML Models for Tabular Data

Decision Trees and Random Forests are some of the most powerful machine learning algorithms for structured and tabular data. In this video, we break down how tree-based models work, why they often outperform neural networks on tabular datasets, and how they make decisions using split criteria like Gini impurity and entropy. You’ll learn how Decision Trees create interpretable rules, how Random Forests reduce overfitting through bagging and feature randomization, and why ensemble learning is so effective for real-world machine learning problems. We’ll also cover pruning, feature importance, and practical implementation strategies using Python. By the end, you’ll understand how to build, tune, and interpret tree-based ML models for classification and regression tasks. Topics covered: Decision Trees, Random Forests, Gini Impurity, Entropy, Bagging, Feature Randomization, Overfitting, Pruning, Feature Importance, Tabular Data Modeling, Machine Learning with Python. Hashtags: #MachineLearning #DecisionTrees #RandomForest #DataScience #AI #Python #MLAlgorithms #TabularData