Building a logistic regression classifier from the ground up
The logistic regression classifier is a widely used machine learning model that predicts the group or category that an observation belongs to. When implementing this model, most people rely on some library or API: just hand over a dataset and out come the predictions. However, I'm not a fan of using black boxes without first understanding what's going on inside. In fact, lifting the hood on this classifier provides a segue to more complex models such as neural networks. Therefore, in this post, I'd like to explore the methodology behind logistic regression classifiers and walk through how to construct one from scratch.