Uncertainty in ML: Probability Noise

What’s Happening

So basically Editor’s note: This article is a part of our series on visualizing the foundations of ML.

Uncertainty in ML: Probability Noise By Matthew Mayo on in Practical ML 0 Post Uncertainty in ML: Probability Noise Image by Author Editor’s note: This article is a part of our series on visualizing the foundations of ML. Welcome to the latest entry in our series on visualizing the foundations of ML. (it feels like chaos)

In this series, we will aim to break down important and often complex technical concepts into intuitive, visual guides to help you master the core principles of the field.

The Details

This entry focuses on the uncertainty, probability, and noise in ML. Uncertainty in ML Uncertainty is an unavoidable part of ML, arising whenever models attempt to make predictions about the real world.

At its core, uncertainty reflects a lack of complete knowledge about an outcome and is most often quantified using probability. Rather than being a flaw, uncertainty is something models must explicitly account for to produce reliable and trustworthy predictions.

Why This Matters

A useful way to think about uncertainty is through the lens of probability and the unknown. Much like flipping a fair coin, where the outcome is uncertain even though the probabilities are well defined, ML models frequently operate in environments where multiple outcomes are possible. As data flows through a model, predictions branch into different paths, influenced by randomness, incomplete information, and variability in the data itself.

This adds to the ongoing AI race that’s captivating the tech world.

Key Takeaways

• The goal of working with uncertainty is not to eliminate it, but to measure and manage it .
• Not all uncertainty is the same.

The Bottom Line

This involves understanding several key components: Probability provides a mathematical framework for expressing how likely an event is to occur Noise represents irrelevant or random variation in data that obscures the true signal and can be either random or systematic Together, these factors shape the uncertainty present in a model’s predictions. Not all uncertainty is the same.

Are you here for this or nah?