Data-preprocessing for Machine Learning model I am confused about how to preprocess range based category such as age, tumor-size &amp; inv-nodes. Should I take an average of the limits, as in - 14.5, 24.5 and so on or do one hot encoding of the co-domain range. Taking one hot encoding would increase the no. of features significantly. Because, each attribute has co-domain as following:


age: 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80-89, 90-99.
tumor-size: 0-4, 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59.
inv-nodes: 0-2, 3-5, 6-8, 9-11, 12-14, 15-17, 18-20, 21-23, 24-26, 27-29, 30-32, 33-35, 36-39.
You should definitely not use one hot encoding with values which represent numbers, as this removes the natural order between your intervals.

So these values should be represented as numbers:


Either with the average of the limits indeed
Or a simple integer encoding of the intervals, e.g. tumor sizes 0-4, 5-9, 10-14,... would be represented as 0,1,2,...