into "bins" as suggested by the following figure. Instead of having one floating-point feature, we now have 11 distinct boolean features (LatitudeBin1, LatitudeBin2, ..., LatitudeBin11). Doing so will enable us to represent latitude 37.4 (San Francisco) as follows: [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0] Thanks to binning, our model can now learn completely different weights for each latitude.