Machine Learning Models as Hypothesis

07 Sep 2021 | One-minute read

It is common when describing neural networks (a subset of machine learning) to use the character \(h\) as the output from a perceptron, as in:

\[h = g(a)\]

h here stands for hypothesis. Literature explains the output of a perceptron as a classification or likelihood in the output given the input.

I like to see analogs in other domains and it occurred that machine learning models are very much hypotheses, such as those from physics. Newton’s second law tells us that the acceleration of an object depends on the mass of the object and the amount of force applied, namely

\[F = ma\]

This is very much a hypothesis. It is a mathematical model that enables us to make predictions. Given an observed mass and acceleration, we can predict the applied force. Einstein’s Theory of Special Relativity is another such hypothesis.

How then does this relate to machine learning models and \(h\)? The machine learned model is nothing more than a hypothesis. It purports to enable us to make predictions about the world. Sometimes the predictions are correct other times the model lacks fidelity and makes incorrect predictions. In all cases though, the output is the result of a hypothesis.