Python for Machine Learning - The Complete Beginners Course - What Is Entropy?

The video tutorial explains the concept of entropy, which measures uncertainty or randomness in data. It uses examples like a fair coin toss and a double-headed coin to illustrate high and zero entropy, respectively. The tutorial also discusses how decision trees utilize entropy to select parameters with high certainty, thereby reducing uncertainty in predictions.

What does entropy tell us about the predictability of an event?

How does the probability of heads and tails in a coin toss relate to entropy?

Explain the difference in entropy between a fair coin and a coin with heads on both sides.

What do lower and higher values of entropy imply about uncertainty?

How does a decision tree utilize entropy when choosing parameters for its nodes?