Python Machine Learning By Example - Second Edition

Binning

Sometimes, it's useful to separate feature values into several bins. For example, we may be only interested whether it rained on a particular day. Given the precipitation values, we can binarize the values, so that we get a true value if the precipitation value isn't zero and a false value otherwise. We can also use statistics to divide values into high, low, and medium bins. In marketing, we often care more about the age group, such as 18 to 24, than a specific age such as 23.

The binning process inevitably leads to loss of information. However, depending on your goals, this may not be an issue, and actually reduces the chance of overfitting. Certainly, there will be improvements in speed and reduction of memory or storage requirements ...