13 Ways Machine Learning Can Steer You Wrong - InformationWeek

Machine learning algorithms running in fully automated systems need the ability to handle missing data points. The most common approach is to use the mean (average) value as a substitute for a missing value. According to Mustafa Bilgic, director of the Machine Learning Lab and associate professor of Computer Science at the Illinois Institute of Technology in Chicago, the approach makes strong assumptions about data, including that the data is "missing at random." For example, said Bilgic in an interview, "The fact that the cholesterol level is missing for a patient actually can be very useful information. It could mean that the test was not ordered on purpose, which could actually mean it is suspected to be either irrelevant for this task or it is assumed to be normal. There are approaches that do not assume the features are'missing at random,' though it is unlikely that the fully automated techniques will know which features are missing at random and which aren't."