A Gentle Introduction to the Central Limit Theorem for Machine Learning
Last Updated on January 14, 2020
The central limit theorem is an often quoted, but misunderstood pillar from statistics and machine learning.
It is often confused with the law of large numbers. Although the theorem may seem esoteric to beginners, it has important implications about how and why we can make inferences about the skill of machine learning models, such as whether one model is statistically better than another and confidence intervals on models skill.
In this tutorial, you will discover the central limit theorem and the implications of this important pillar of statistics and probability on applied machine learning.
After completing this tutorial, you will know:
- The central limit theorem describes the shape of the distribution of sample means as a Gaussian, which is a distribution that statistics knows a lot about.
- How to develop an example of simulated dice rolls in Python to demonstrate the central limit theorem.
- How the central limit theorem and knowledge of the Gaussian distribution is used to make inferences about model performance in applied machine learning.
Kick-start your project with my new book Statistics for Machine Learning, including step-by-step tutorials and the Python source code files for all examples.