Machine Learning Demystified, Part 3: Models

Great, you've just identified some of the characteristics of a good generalization: a relatively simple, abstract, less detailed, model that is consistent with (or fits, or explains) the observations, and is more broadly applicable beyond the cases you have seen. That's how humans think, but how does this help us design a computer algorithm that generalizes well? This helps us in a couple of ways. Firstly, it gives us a framework for designing an ML algorithm. Just like humans build mental models based on their observations, an ML algorithm should ingest training data and output a model that fits, or explains the training data well. A model is the mathematical analog to the human idea of a "concept" or "mental model"; it's the mathematical formalization of what we have been informally calling "rules" until now. A model is essentially a function takes as input the characteristics (i.e.