[TBD]Role of Chebyshev Inequality In Machine Learning

Introduction

Laymen explanation

Technical explanation

In probability theory, Chebyshev's inequality (also called the Bienaymé–Chebyshev inequality) guarantees that, for a wide class of probability distributions, no more than a certain fraction of values can be more than a certain distance from the mean. Specifically, no more than 1/k2 of the distribution's values can be more than k standard deviations away from the mean (or equivalently, at least 1 − 1/k2 of the distribution's values are within k standard deviations of the mean).

Mathematical explanation

Below is the example for K=2

Benefits

Role in machine learning

Use in neural networks

Reference

https://youtu.be/DWsDqKlW7Z4

https://en.wikipedia.org/wiki/Chebyshev%27s_inequality

https://medium.com/analytics-vidhya/basic-understanding-of-gaussian-distribution-and-chebyshevs-inequality-ba57ba79a635

https://images.app.goo.gl/dq3eXqTdx1hLbbfPA