Why Do I Get Different Results Each Time in Machine Learning?

Last Updated on August 27, 2020

Are you getting different results for your machine learning algorithm?

Perhaps your results differ from a tutorial and you want to understand why.

Perhaps your model is making different predictions each time it is trained, even when it is trained on the same data set each time.

This is to be expected and might even be a feature of the algorithm, not a bug.

In this tutorial, you will discover why you can expect different results when using machine learning algorithms.

After completing this tutorial, you will know:

• Machine learning algorithms will train different models if the training dataset is changed.
• Stochastic machine learning algorithms use randomness during learning, ensuring a different model is trained each run.
• Differences in the development environment, such as software versions and CPU type, can cause rounding error differences in predictions and model evaluations.

Let’s get started.