I've mentioned both in the DS Modeling Tutorial and in another article of mine the importance of discretization / binning of a continuous variable, as a strategy for turning it into a feature, to be used in a data model. However, how meaningful and information-rich the resulting categorical feature is going to be depends on the thresholds we use. In this post I'd like to share with you a strategy that I've come up with that works well in doing just that.
First of all, we need to make sure we have a potent method for calculating the density of a data point. I'm not talking about probability density though, since the latter is a statistical concept that has more to do with the mathematical form of a distribution than the actual density observed. The actual density is what we would measure if we were to look at the data itself and although it's quite straight-forward, it's not as easy to do at scale. That's why I first developed a very simple (almost simplistic) method for approximating density using a sampling of sorts, rather than looking at each individual element in the variable.
Afterwards, we just need to figure out the point of least density, that's not an extreme of the variable. In other words, identity of a local minimum in the density distribution, a fairly easy task that's also computationally cheap. Of course it's good to have a threshold too, to distinguish between this point being an actual low-density point and one that could be due to chance. If the density of that point is below this threshold, we can take it to be a point of dissection for the variable, effectively binarizing it.
Beyond that, we can repeat the same process recursively, for the two partitions of the variable. This way, we can end up with 3, 4, or even 100 partitions at the end of the process. This is another reason why this aforementioned threshold is very important. After all, not all partitions would be binarizable in a meaningful way. Also, it would be a good idea to have a limit to how many partitions overall we allow, so that we don't end up with a categorical variable having 1000 unique values either!
This optimal discretization / binning process is very simple and robust, resulting into a simpler form of the original variable, one that can be broken down to a set of binary features afterwards, if needed. This can also be useful in identifying potential outliers and being able to use them (as separate values in the new feature) instead of discarding them. The method is made even faster through its implementation in Julia, which once again proved itself as a great DS tool.
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.