Predictive Analytics with TensorFlow 2.3: Using Information Theory in Predictive Modeling

The video tutorial introduces information theory, highlighting its applications in various fields such as communication, medical science, and machine learning. It explains key concepts like mutual information, entropy, and information gain, and their roles in predictive modeling. The tutorial also covers the use of information theory in machine learning, particularly in decision trees and neural networks. Additionally, it introduces a Python module for implementing information theory concepts, providing examples of its application.

Describe conditional entropy and its significance.

What are some practical applications of information theory in machine learning?

How is information gain used in decision tree algorithms?