A Gentle Introduction to the Law of Large Numbers in Machine Learning

Last Updated on August 8, 2019

We have an intuition that more observations is better.

This is the same intuition behind the idea that if we collect more data, our sample of data will be more representative of the problem domain.

There is a theorem in statistics and probability that supports this intuition that is a pillar of both of these fields and has important implications in applied machine learning. The name of this theorem is the law of large numbers.

In this tutorial, you will discover the law of large numbers and why it is important in applied machine learning.

After completing this tutorial, you will know:

• The law of large numbers supports the intuition that the sample becomes more representative of the population as its size is increased.
• How to develop a small example in Python to demonstrate the decrease in error from the increase in sample size.
• The law of large numbers is critical for understanding the selection of training datasets, test datasets, and in the evaluation of model skill in machine learning.

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