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 is not zero, and a false value otherwise. We can also use statistics to divide values into high, low, and medium bins.

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