I never guess. It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.
—Sir Arthur Conan Doyle, author of Sherlock Holmes stories
Predictive modeling is one of the most common data mining tasks. As the name implies, it is the process of taking historical data (the past), identifying patterns in the data that are seen though some methodology (the model), and then using the model to make predictions about what will happen in the future (scoring new data).
Data mining is a composite discipline that overlaps other branches of science. In Figure 4.1, we can see the contributions of many different fields in the development of the science of data mining. Because of the contributions of many disciplines, staying up to date on the progress being made in the field of data mining is a continuous educational challenge. In this section I discuss algorithms that come primarily from statistics and machine learning. These two groups largely live in different university departments (statistics and computer science respectively) and in my opinion are feuding about the best way to prepare students for the field of data science. Statistics departments teach a great deal of theory but produce students with limited programming skills. Computer science departments produce great programmers with a solid understanding of how computer languages interact with computer hardware but ...