Decision Trees in Machine Learning Simplified - Part 1 | Machine Learning Tutorial for Beginners

In this video on "Decision Trees in Machine Learning Simplified - Part 1 | Machine Learning Tutorial for Beginners," we introduce the basics of Decision Trees, a popular supervised learning algorithm. We explain how Decision Trees work, their structure, and why they are widely used for classification and regression tasks in Machine Learning.

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This video on "Decision Trees in Machine Learning Simplified - Part 1 | Machine Learning Tutorial for Beginners" includes the following topics:

00:00 Introduction
00:15 What Are Decision Trees?
00:33 Real-Life Example
01:08 Applying Decision Trees to Machine Learning
01:26 Key Components
02:17 Why Decision Trees Are Popular in Machine Learning?
02:50 Conclusion

What is a Decision Tree in Machine Learning?
A Decision Tree is a flowchart-like structure where internal nodes represent features, branches represent decision rules, and leaf nodes represent outcomes. It splits data into subsets based on feature values, making it easy to interpret and visualise decision-making processes.

How are Decision Trees used in classification and regression?
Decision Trees can be used for both classification and regression tasks by learning decision rules from data features. In classification, they predict categorical outcomes, while in regression, they predict continuous values. This video demonstrates how to build and evaluate Decision Trees using Python.

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Видео Decision Trees in Machine Learning Simplified - Part 1 | Machine Learning Tutorial for Beginners канала The Knowledge Academy