Machine Learning in Java - Second Edition

Filling missing values

Machine learning algorithms generally do not work well with missing values. Rare exceptions include decision trees, Naive Bayes classifier, and some rule-based learners. It is very important to understand why a value is missing. It can be missing due to many reasons, such as random error, systematic error, and sensor noise. Once we identify the reason, there are multiple ways to deal with the missing values, as shown in the following list:

• Remove the instance: If there is enough data, and only a couple of non-relevant instances have some missing values, then it is safe to remove these instances.
• Remove the attribute: Removing an attribute makes sense when most of the values are missing, values are constant, or an attribute ...