Arithmetic, Geometric, and Harmonic Means for Machine Learning

Last Updated on August 19, 2020

Calculating the average of a variable or a list of numbers is a common operation in machine learning.

It is an operation you may use every day either directly, such as when summarizing data, or indirectly, such as a smaller step in a larger procedure when fitting a model.

The average is a synonym for the mean, a number that represents the most likely value from a probability distribution. As such, there are multiple different ways to calculate the mean based on the type of data that you’re working with.

This can trip you up if you use the wrong mean for your data. You may also enter some of these more exotic calculations of mean values when using performance metrics to evaluate your model, such as the G-mean or the F-Measure.

In this tutorial, you will discover the difference between the arithmetic mean, the geometric mean, and the harmonic mean.

After completing this tutorial, you will know:

• The central tendency summarizes the most likely value for a variable, and the average is the common name for the calculation of the mean.
• The arithmetic mean is appropriate if the values have the
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