Skip to content
Generic filters
Exact matches only

Do Not Use Random Guessing As Your Baseline Classifier

Last Updated on September 25, 2019

I recently received the following question via email:

Hi Jason, quick question. A case of class imbalance: 90 cases of thumbs up 10 cases of thumbs down. How would we calculate random guessing accuracy in this case?

We can answer this question using some basic probability (I opened excel and typed in some numbers).

Discover bayes opimization, naive bayes, maximum likelihood, distributions, cross entropy, and much more in my new book, with 28 step-by-step tutorials and full Python source code.

Let’s get started.

Note, for a more detailed tutorial on this topic, see:

Don't Use Random Guessing As Your Baseline Classifier

Don’t Use Random Guessing As Your Baseline Classifier
Photo by cbgrfx123, some rights reserved.

Let’s say the split is 90%-10% for class 0 and class 1. Let’s also say that you will guess randomly using the same ratio.

The theoretical accuracy of random guessing on a two-classification problem is:

= P(class is 0) * P(you guess 0) + P(class is 1) * P(you guess 1)

= P(class is 0) * P(you guess 0) + P(class is 1) * P(you guess 1)

We can test this on our example 90%-10% split:

= (0.9 * 0.9) + (0.1 * 0.1)
= 0.82
= 0.82 * 100 or 82%

= (0.9 * 0.9) + (0.1 * 0.1)

= 0.82

= 0.82 * 100 or 82%

To check the math, you can plug-in a 50%-50% split of your data and it matches your intuition:

= (0.5 * 0.5) + (0.5 * 0.5)
= 0.5
= 0.5 * 100 or 50%

= (0.5 * 0.5) + (0.5 * 0.5)

= 0.5

= 0.5 * 100 or 50%

If we look on Google, we find a similar question on Cross Validated “What is the chance level accuracy in unbalanced classification problems?” with an almost identical answer. Again, a nice confirmation.

Interesting, but there is an important takeaway point from all of this.

Want to Learn Probability for Machine Learning

Take my free 7-day email crash course now (with sample code).

Click to sign-up and also get a free PDF Ebook version of the course.

Download Your FREE Mini-Course

Don’t Use Random Guessing As A Baseline

If you are looking for a classifier to use as a baseline accuracy, don’t use random guessing.

There is a classifier called Zero Rule (or 0R or ZeroR for short). It is the simplest rule you can use on a classification problem and it simply predicts the majority class in your dataset (e.g. the mode).

In the example above with a 90%-10% for class 0 and class 1 it would predict class 0 for every prediction and achieve an accuracy of 90%. This is 8% better than the theoretical maximum using random guessing.

Use the Zero Rule method as a baseline.

Also, in imbalanced classification problems like this, you should use metrics other than Accuracy such as Kappa or Area under ROC Curve.

For more information about alternative performance measures on classification problems see the post:

For more on working with imbalanced classification problems see the post:

Do you have any questions about this post? Ask in the comments.

Get a Handle on Probability for Machine Learning!

Probability for Machine Learning

Develop Your Understanding of Probability

…with just a few lines of python code

Discover how in my new Ebook:
Probability for Machine Learning

It provides self-study tutorials and end-to-end projects on:
Bayes Theorem, Bayesian Optimization, Distributions, Maximum Likelihood, Cross-Entropy, Calibrating Models
and much more…

Finally Harness Uncertainty in Your Projects

Skip the Academics. Just Results.

See What’s Inside

About Jason Brownlee

Jason Brownlee, PhD is a machine learning specialist who teaches developers how to get results with modern machine learning methods via hands-on tutorials.

error: Content is protected !!