Teaching Your Computer

As I have written in my last two articles (Machine Learning Everywhere and Preparing Data for Machine Learning), machine learning is influencing our lives in numerous ways. As a consumer, you've undoubtedly experienced machine learning, whether you know it or not—from recommendations for what products you should buy from various online stores, to the selection of postings that appear (and don't) on Facebook, to the maddening voice-recognition systems that airlines use, to the growing number of companies that offer to select clothing, food and wine for you based on your personal preferences.

Machine learning is everywhere, and although the theory and practice both can take some time to learn and internalize, the basics are fairly straightforward for people to learn.

The basic idea behind machine learning is that you build a model—a description of the ways the inputs and outputs are related. This model then allows you to ask the computer to analyze new data and to predict the outputs for new sets of inputs. This is essentially what machine learning is all about. In "supervised learning", the computer is trained to categorize data based on inputs that humans had previously categorized. In "unsupervised learning", you ask the computer to categorize data on your behalf.

In my last article, I started exploring a data set created by Scott Cole, a data scientist (and neuroscience PhD student) who measured burritos in a variety of California restaurants. I looked at the different categories of data that Cole and his fellow eater-researchers gathered and considered a few ways one could pare down the data set to something more manageable, as well as reasonable.

Here I describe how to take this smaller data set, consisting solely of the features that were deemed necessary, and use it to train the computer by creating a machine-learning model.

Machine-Learning Models

Let's say that the quality of a burrito is determined solely by its size. Thus, the larger the burrito, the better it is; the smaller the burrito, the worse it is. If you describe the size as a matrix X, and the resulting quality score as y, you can describe this mathematically as:


y = qX

where q is a factor describing the relationship between X and y.

Of course, you know that burrito quality has to do with more than just the size. Indeed, in Cole's research, size was removed from the list of features, in part because not every data point contained size information.

Moreover, this example model will need to take several factors—not just one—into consideration, and may have to combine them in a sophisticated way in order to predict the output value accurately. Indeed, there are numerous algorithms that can be used to create models; determining which one is appropriate, and then tuning it in the right way, is part of the game.

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