What Does Stochastic Mean in Machine Learning?

Last Updated on July 24, 2020

The behavior and performance of many machine learning algorithms are referred to as stochastic.

Stochastic refers to a variable process where the outcome involves some randomness and has some uncertainty. It is a mathematical term and is closely related to “randomness” and “probabilistic” and can be contrasted to the idea of “deterministic.”

The stochastic nature of machine learning algorithms is an important foundational concept in machine learning and is required to be understand in order to effectively interpret the behavior of many predictive models.

In this post, you will discover a gentle introduction to stochasticity in machine learning.

After reading this post, you will know:

• A variable or process is stochastic if there is uncertainty or randomness involved in the outcomes.
• Stochastic is a synonym for random and probabilistic, although is different from non-deterministic.
• Many machine learning algorithms are stochastic because they explicitly use randomness during optimization or learning.

Kick-start your project with my new book Probability for Machine Learning, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

To finish reading, please visit source site