Discrete entropies

1. Is entropy discrete or continuous?
2. What is discrete distribution with example?
3. What is data entropy?
4. What does entropy of 1 mean?

Is entropy discrete or continuous?

Entropy as well as entropy preservation is well-defined only in the context of purely discrete or continuous random variables. However for a mixture of discrete and continuous random variables, which arise in many interesting situations, the notions of entropy and entropy preservation have not been well understood.

What is discrete distribution with example?

A discrete probability distribution counts occurrences that have countable or finite outcomes. This is in contrast to a continuous distribution, where outcomes can fall anywhere on a continuum. Common examples of discrete distribution include the binomial, Poisson, and Bernoulli distributions.

What is data entropy?

In information theory, the entropy of a random variable is the average level of "information", "surprise", or "uncertainty" inherent to the variable's possible outcomes.

What does entropy of 1 mean?

This is considered a high entropy , a high level of disorder ( meaning low level of purity). Entropy is measured between 0 and 1. (Depending on the number of classes in your dataset, entropy can be greater than 1 but it means the same thing , a very high level of disorder.