O'Reilly logo

Practical Predictive Analytics by Ralph Winters

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Correcting for missing values

Although it is always important to understand the source of your missing values, how you ultimately handle them depends upon the technique that you use to analyse your data sets. For example, classification methods such as decision trees and random forests know how to deal with missing values, since they can treat them as a separate class, and you can safely leave them in the model. However, if a variable has a large amount of missing values, say > 20%, you might want to look at imputation techniques, or try to find a better variable that measures the same thing.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required