12 - Random Forests | Machine Learning Distilled скачать в хорошем качестве

12 - Random Forests | Machine Learning Distilled

In this video, we explore Random Forests, a powerful ensemble learning method that builds on decision trees to deliver better performance and accuracy. You’ll learn: 🌲 What Random Forests are and how they extend decision trees 👥 The concept of ensemble methods and why combining models works ⚙️ How Random Forests are constructed (bagging, feature sampling, and multiple trees) 🗳️ How predictions are made with majority voting and averaging 📊 The importance of features and how Random Forests help with selection ⚖️ Why Random Forests reduce variance and improve generalization 🔧 Key parameters: number of trees (T), features (m), and training samples (n) By the end, you’ll understand why Random Forests are one of the most widely used algorithms in machine learning and data science. ⸻ ⏱️ Timestamps (INCORRECT) 00:00 – Introduction to Random Forests 01:15 – Ensemble Methods Explained 03:00 – Random Forest Construction Process 05:30 – How Random Forests Make Predictions 07:00 – Variable Importance & Feature Selection 08:40 – Why Random Forests Work So Well 10:20 – Key Parameters and Tuning 12:00 – Summary & Recap ⸻ 📌 Hashtags #RandomForest #MachineLearning #AI #ArtificialIntelligence #DataScience #MLBasics #EnsembleLearning #DecisionTrees #FeatureSelection #LearnMachineLearning #MachineLearningAlgorithms -- An accessible intro to basic, classical machine learning algorithms. The material is somewhat dated but may still have some value for ML foundations. This video course was originally published on TutsPlus in 2013.