– Attribute selection techniques（00:04:00 - 00:05:26）
Decision Trees Explained | Introduction to Decision Trees

The decision trees algorithm belongs to the family of supervised learning algorithms. Just like other supervised learning algorithms, decision trees model relationships, and dependencies between the predictive outputs and the input features.

As the name suggests, the decision tree algorithm is a tree-structured classifier preferably used to solve classification problems. When a decision tree categorizes data into different classes it is called a classification tree. When it predicts numeric values, it is known as a regression tree.

Interestingly, decision tree algorithms are used for regression models as well. The same library that you would use to build a classification model, can also be used to build a regression model after changing some of the parameters. We use Attribute Selection Method techniques to simplify the decision-making process and calculate values for every attribute.

Watch the video to learn in detail about Decision Trees.

Table of Contents:
0:00 – Introduction to discussion topics
0:28 – Learning about supervised learning algorithm
01:52 – Classification of nodes
03:09 – Real-life example to understand decision tree
04:00 – Attribute selection techniques
05:26 – Design of a decision tree
06:15 – Problem with decision trees

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