How To Choose The Right Test Options When Evaluating Machine Learning Algorithms

Last Updated on June 21, 2016

The test options you use when evaluating machine learning algorithms can mean the difference between over-learning, a mediocre result and a usable state-of-the-art result that you can confidently shout from the roof tops (you really do feel like doing that sometimes).

In this post you will discover the standard test options you can use in your algorithm evaluation test harness and how to choose the right options next time.

Randomness

The root of the difficulty in choosing the right test options is randomness. Most (almost all) machine learning algorithms use randomness in some way. The randomness may be explicit in the algorithm or may be in the sample of the data selected to train the algorithm.

This does not mean that the algorithms produce random results, it means that they produce results with some noise or variance. We call this type of limited variance, stochastic and the algorithms that exploit it, stochastic algorithms.

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