41. Random Forest Algorithm

Описание: 📌 Video Description

The Random Forest Algorithm is a powerful ensemble learning method used in data science and machine learning to improve prediction accuracy and reduce overfitting. In this video, we explain how random forests combine multiple decision trees into a single, more reliable model.

You’ll learn how each decision tree is trained on different subsets of data and features, and how their individual predictions are combined using majority voting (for classification) or averaging (for regression). By leveraging the wisdom of many models instead of one, random forests achieve higher accuracy, stability, and robustness.

This lesson builds on decision trees and introduces one of the most widely used algorithms in practical machine learning.

🎯 What You’ll Learn in This Video

What the Random Forest algorithm is

Why ensemble methods outperform single models

How multiple decision trees are built

What majority voting means in classification

How random forests reduce overfitting

Advantages and limitations of random forests

Real-world use cases of random forests

👨‍💻 Who This Video Is For

Data science and machine learning beginners

Students preparing for exams

Data analysts and ML practitioners

Anyone learning ensemble learning techniques

🔑 Keywords (SEO)

Random Forest Algorithm, Ensemble Learning, Decision Trees Ensemble, Majority Voting, Machine Learning Algorithms, Data Science Fundamentals, Tree Based Models, Learn Random Forest

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📌 Hashtags

#RandomForest #EnsembleLearning #MachineLearning #DecisionTrees #DataScience #LearnDataScience #TechFundamentals