The curse of dimensionality! What on earth is that? Besides being a prime example of shock-and-awe names in machine learning jargon (which often sound far fancier than they are), it’s a reference to the effect that adding more features has on your dataset. In a nutshell, the curse of dimensionality is all about loneliness.

In a nutshell, the curse of dimensionality is all about loneliness.

Before I explain myself, let’s get some basic jargon out of the way. What’s a feature? It’s the machine learning word for what other disciplines might call a predictor / (independent) variable / attribute / signal. Information about each datapoint, in other words. Here’s a jargon intro if none of those words felt familiar.

Data social distancing is easy: just add a dimension. But for some algorithms, you may find that this is a curse…

When a machine learning algorithm is sensitive to the curse of dimensionality, it means the algorithm works best when your datapoints are surrounded in space by their friends. The fewer friends they have around them in space, the worse things get. Let’s take a look.

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