Discussing the article: "Overcoming The Limitation of Machine Learning (Part 3): A Fresh Perspective on Irreducible Error"

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Check out the new article: Overcoming The Limitation of Machine Learning (Part 3): A Fresh Perspective on Irreducible Error.

This article takes a fresh perspective on a hidden, geometric source of error that quietly shapes every prediction your models make. By rethinking how we measure and apply machine learning forecasts in trading, we reveal how this overlooked perspective can unlock sharper decisions, stronger returns, and a more intelligent way to work with models we thought we already understood.

This article will introduce the reader to advanced limitations of current machine learning models that are not explicitly taught to instructors before they deploy these models. The field of machine learning is dominated by mathematical notation and literature. And since there are many levels of abstraction from which a practitioner can study, the approach often differs. For example, some practitioners study machine learning simply from high-level libraries such as scikit-learn, which provide an easy and intuitive framework to use models while abstracting away the mathematical concepts that underpin them.

However, depending on the level of mastery and the amount of control the practitioner desires, sometimes these abstractions must be removed to see what’s really going on under the hood. Therefore, in any project involving machine learning models, irreducible error is always present, though it is rarely mentioned directly.

Author: Gamuchirai Zororo Ndawana