The website is in Maintenance mode. We are in the process of adding more features.
Any new bookmarks, comments, or user profiles made during this time will not be saved.

Machine Learning Resources

What is Discretization? When is doing discretization better as opposed to using continuous variable?

Bookmark this question

Discretization refers to the process of binning a continuous variable into a discrete number of buckets. In some machine learning algorithms, the performance can be improved by using this kind of representation, especially if there are outliers on the original scale of the variable that cause its distribution to be skewed. There are different ways to perform discretization, but common approaches include equal width bins, where the spacing between endpoints is constant; equal size bins, where each bin contains roughly equal number of observations even if the endpoints are not spaced uniformly; or using a decision tree to create bins that are most predictive of the target variable. 

Leave your Comments and Suggestions below:

Please Login or Sign Up to leave a comment

Partner Ad  

Find out all the ways
that you can

Explore Questions by Topics

Partner Ad

Learn Data Science with Travis - your AI-powered tutor |