1/13/2024 0 Comments Information entropy, Machine Learning: A Probabilistic Perspective, 2012.Ī foundational concept from information is the quantification of the amount of information in things like events, random variables, and distributions. Information theory is concerned with representing data in a compact fashion (a task known as data compression or source coding), as well as with transmitting and storing it in a way that is robust to errors (a task known as error correction or channel coding). The field was proposed and developed by Claude Shannon while working at the US telephone company Bell Labs. It is a subfield of mathematics and is concerned with topics like data compression and the limits of signal processing. Information theory is a field of study concerned with quantifying information for communication. Calculate the Entropy for a Random Variable.This tutorial is divided into three parts they are: Photo by Cristiano Medeiros Dalbem, some rights reserved. Update Nov/2019: Added example of probability vs information and more on the intuition for entropy.Ī Gentle Introduction to Information Entropy.Kick-start your project with my new book Probability for Machine Learning, including step-by-step tutorials and the Python source code files for all examples. Entropy provides a measure of the average amount of information needed to represent an event drawn from a probability distribution for a random variable.Information provides a way to quantify the amount of surprise for an event measured in bits.Information theory is concerned with data compression and transmission and builds upon probability and supports machine learning.In this post, you will discover a gentle introduction to information entropy. As such, a machine learning practitioner requires a strong understanding and intuition for information and entropy. More generally, this can be used to quantify the information in an event and a random variable, called entropy, and is calculated using probability.Ĭalculating information and entropy is a useful tool in machine learning and is used as the basis for techniques such as feature selection, building decision trees, and, more generally, fitting classification models. Information theory is a subfield of mathematics concerned with transmitting data across a noisy channel.Ī cornerstone of information theory is the idea of quantifying how much information there is in a message.
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