Predictive Modeling, Supervised Machine Learning, and Pattern Classification -- the big picture

When I was working on my next pattern classification application, I realized that it might be worthwhile to take a step back and look at the big picture of pattern classification in order to put my previous topics into context and to provide and introduction for the future topics that are going to follow. Pattern classification and machine learning are very hot topics and used in almost every modern application: Optical Character Recognition (OCR) in the post office, spam filtering in our email clients, barcode scanners in the supermarket … the list is endless. In this article, I want to give a quick overview about the main concepts of a typical supervised learning task as a primer for future articles and implementations of various learning algorithms and applications. Predictive modeling is the general concept of building a model that is capable of making predictions. Typically, such a model includes a machine learning algorithm that learns certain properties from a training dataset in order to make those predictions.