It seems a safe assumption that the proverbial correlation does not equal causation—a dead horse has been sufficiently beaten. Or has it? It is quite apparent that correlation-to-causation leaps of faith are still an issue in the real world. As a result, we must remember and convey with conviction that these algorithms are based on observational and not experimental data. Regardless of what correlations we find via machine learning, nothing can trump a proper experimental design. As Professor Domingos states:
If we find that beer and diapers are often bought together at the supermarket, then perhaps putting beer next to the diaper section will increase sales. But short of actually doing the experiment it's difficult to tell." | ||
| --Domingos, P., 2012) | ||
In Chapter 11, Time Series and Causality, we will touch on a technique borrowed from econometrics to explore causality in time series, tackling an emotionally and politically sensitive issue.
Enough of my waxing philosophically; let's get started with using R to master machine learning! If you are a complete novice to the R programming language, then I would recommend that you skip ahead and read the appendix on using R. Regardless of where you start reading, remember that this book is about the journey to master machine learning and not a destination in and of itself. As long as we are working in this field, there will always be something new and exciting to explore. As such, I look forward to receiving your comments, thoughts, suggestions, complaints, and grievances. As per the words of the Sioux warriors: Hoka-hey! (Loosely translated it means forward together)