Imagine you are building a model that can identify cats in photographs. You collect thousands of images, choose a neural network, train it, and check whether it predicts correctly. Everything seems straightforward, until someone asks, “But what exactly is the model doing?” This is where Marr’s three levels of analysis become useful. Instead of looking…
Month: September 2026
ML Models Only Speak Numbers. So How Do We Make Non-Numerics Predictive?
These “non-numerics” are often referred to as Categorical Features in machine learning. Almost every feature set we use for our ML model contains some categorical features that have the potential of becoming great predictors, but, because the model can only work with numerics, they often go underused. Encoding in machine learning give us a way…