Chapter 11. Machine Learning
I am always ready to learn although I do not always like being taught.
Many people imagine that data science is mostly machine learning and that data scientists mostly build and train and tweak machine-learning models all day long. (Then again, many of those people don’t actually know what machine learning is.) In fact, data science is mostly turning business problems into data problems and collecting data and understanding data and cleaning data and formatting data, after which machine learning is almost an afterthought. Even so, it’s an interesting and essential afterthought that you pretty much have to know about in order to do data science.
Before we can talk about machine learning we need to talk about models.
What is a model? It’s simply a specification of a mathematical (or probabilistic) relationship that exists between different variables.
For instance, if you’re trying to raise money for your social networking site, you might build a business model (likely in a spreadsheet) that takes inputs like “number of users” and “ad revenue per user” and “number of employees” and outputs your annual profit for the next several years. A cookbook recipe entails a model that relates inputs like “number of eaters” and “hungriness” to quantities of ingredients needed. And if you’ve ever watched poker on television, you know that they estimate each player’s “win probability” in real time based on a model that takes into account ...