# What is the best approach to transform scale from 1 to 10 into three categories?

I want discretize my attributes according to the class quality which is the output variable. quality ranges from 1 to 10 in my data set. I think it'd be nice to have three quality categories: low, med, high. I defined low as 1-3, med as 4-6 and high as 7-10. However, the distribution of those categories is as follows:

There're very few instances with low quality. What would be the best approach to deal with those values? Should I discard them altogether, divide quality class into only 2 categories: not-high and high or pursue another approach?

EDIT: this is the dataset I'm analyzing: http://archive.ics.uci.edu/ml/datasets/Wine+Quality.

• Not content with throwing away information, you want to throw away even more. I want to ask Why. Please try this question. I have information on people's heights. Not many people are more than 2 metres tall, so should I just lump those in with shorter people? There could be a rationale for using a coarser classification, but unless you tell us what this is, this is just a proposal to degrade data. – Nick Cox Mar 31 at 10:43