Lecture 3.1 | Decision Tree Learning | Inductive Bias, Entropy, Gini Impurity, Information Gain
Welcome to our comprehensive tutorial on Decision Tree Learning! In this in-depth YouTube video, we provide a comprehensive guide to mastering Decision Tree Learning, covering its introduction, inductive bias, inductive inference, entropy, Gini impurity, information gain, and common issues faced during implementation.
Decision Tree Learning is a widely used machine learning algorithm for classification and regression tasks. It constructs a tree-like model that represents decisions and their possible consequences based on input features.
In this tutorial, we explore the following key topics related to Decision Tree Learning:
00:00 - Agenda
01:40 - Introduction to Decision tree algorithm
05:20 - Inductive Bias
10:45 - Inductive inference
13:20 - Example with Tennis Dataset
19:02 - Entropy
21:58 - Gini Impurity
24:14 - Entropy v/s Gini Impurity
25:28 - Information Gain
30:55 - Common Issues
By the end of this tutorial, you will have a solid understanding of Decision Tree Learning, including its introduction, inductive bias, inductive inference, entropy, Gini impurity, information gain, and how to tackle common issues.
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