Key Points to Remember

Machine learning algorithms come with the promise of being able to figure out how to perform important tasks by learning from data, i.e., generalizing from examples without being explicitly told what to do. This means that the higher the amount of data, the more ambitious problems can be tackled by these algorithms. However, developing successful machine learning applications requires quite some “black art” that is hard to find.

Let’s go through some of the lessons learned by machine learning researchers and practitioners (put together in a great research paper by Professor Pedro Domingos), so that we can avoid some of the major pitfalls.

1. It’s Generalization That Counts

The fundamental goal of machine learning is to generalize beyond the examples in the training set. No matter how much data we have, it is very unlikely that we will see those exact examples again at test time. Doing well on the training set is easy. The most common mistake among beginners is to test on the training data and have the illusion of success. If the chosen classifier is then tested on new data, it is often no better than random guessing. So, set some of the data aside from the beginning, and only use it to test ...