Classifying data with logistic regression

As seen in the last chapter, we trained the tree-based models only based on the first 300,000 samples out of 40 million. We did so simply because training a tree on a large dataset is extremely computationally expensive and time-consuming. Since we are now not limited to algorithms directly taking in categorical features thanks to one-hot encoding, we should turn to a new algorithm with high scalability to large datasets. Logistic regression is one of the most, or perhaps the most, scalable classification algorithms.

Getting started with the logistic function

Let's start with an introduction to the logistic function (which is more commonly referred to as the sigmoid function)...