We understand Machine Learning, a subset of Artificial Intelligence, as a computer being programmed with the ability to self-learn and improve itself on a particular task. Supervised Learning in Machine Learning allows one to produce or collect data based on previous experience. It helps one to optimize performance criteria using past experience and work on real-time computational problems.

Great Learning brings you this tutorial on Classification using Decision Trees where we understand how classification can be implemented with decision trees using R language. This video discusses the advantages of using tree-based models, followed by looking at a case study to better understand the topic. Then we look at the Gini index, entropy and misclassification error. Following this, we will look at the concept of measuring impurity. Finally, we look at the types of decision tree algorithms! This video teaches Classification using Decision Trees and their key functions and concepts with a variety of demonstrations & examples.

• 0:00:00 Introduction
• 0:01:07 What is Classification?
• 0:03:40 What is Decision Tree Learning?
• 0:15:50 Advantages of using Tree-based Models
• 0:24:46 Gold Loan Case Study
• 1:00:14 Decision Tree in-depth Concept
• 1:16:27 Gini Index, Entropy, Misclassification Error
• 1:31:02 Measuring Impurity
• 1:56:21 Types of Decision Tree algorithms

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