Decision Tree CART - Machine Learning Fun and Easy

The importance of decision trees and the practical application of classification and regression trees (CART). Watch this video to learn the importance of decision trees and why they are so practical to use.

Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. A tree has many analogies in real life, and turns out that it has influenced a wide area of machine learning, covering both classification and regression trees (CART).

So a decision tree is a flow-chart-like structure, where each internal node denotes a test on an attribute, each branch represents the outcome of a test, and each leaf (or terminal) node holds a class label. The topmost node in a tree is the root node.
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0:00 Introduction
0:13 SUPERVISED MACHINE LEARNING ALGORITHM
1:42 DISADVANTAGES OF CART
2:30 APPLICATIONS OF DECISION TREE
3:07 DIFFERENCES AND SIMILIARITIES BETWEEN
4:26 HOW CAN AN ALGORITHM BE REPRESENTED BY A TREE?
5:03 GROWING A TREE
6:28 EXAMPLE