Categorical variables in distance computations

<p>Doing this mapping results in a dimensional expansion of the problem space (3 dimensions instead of 1), though those dimensions are flatter. Well, the dimensions are not flatter; those are flat by definition. But the geometry of the problem is (flatter). We like to think of this as&nbsp;<em>unwrapping</em>&nbsp;a categorical variable into higher dimensions.</p> <p>This dimensional expansion can become a problem if we have many categorical variables and/or many categories per variable. We might expand the problem&rsquo;s dimensions so much that we end up with the number of (new) variables greater than the number of data points&hellip; which is tractable, but less than ideal.</p> <p><a href="https://medium.com/@nttp/categorical-variables-in-distance-computations-b87de832e4ed"><strong>Website</strong></a></p>