Machine Learning Demystified, Part 3: Models

In Part 2 of our series, the ML novice realized that generalization is a key ingredient of a true ML algorithm. In today’s post we continue the conversation (the novice is in bold italics) to elaborate on generalization and take a first peek at how one might design an algorithm that generalizes well. Last time we started talking about how one would design an ML algorithm to predict the click-through-rate (CTR) on an ad impression, and you designed two extreme types of algorithms. The first one was the constant algorithm, which simply records the overall historical click-rate a, and predicts a for every new ad opportunity. This was fine for overall accuracy but would do poorly on individual ad impressions, because its predictions are not differentiated based on the attributes…


Link to Full Article: Machine Learning Demystified, Part 3: Models