Confidence Intervals for Machine Learning

Last Updated on August 8, 2019

Much of machine learning involves estimating the performance of a machine learning algorithm on unseen data.

Confidence intervals are a way of quantifying the uncertainty of an estimate. They can be used to add a bounds or likelihood on a population parameter, such as a mean, estimated from a sample of independent observations from the population. Confidence intervals come from the field of estimation statistics.

In this tutorial, you will discover confidence intervals and how to calculate confidence intervals in practice.

After completing this tutorial, you will know:

• That a confidence interval is a bounds on an estimate of a population parameter.
• That the confidence interval for the estimated skill of a classification method can be calculated directly.
• That the confidence interval for any arbitrary population statistic can be estimated in a distribution-free way using the bootstrap.

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.

Let’s get started.

• Update Jun/2018: Fixed a typo in sampling part of the bootstrap code example.

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