In practice, data typically contains errors and imperfections due to various reasons such as measurement errors, human mistakes, and errors of expert judgment in classifying training examples. We refer to all of these as noise. Noise can also come from the treatment of missing values when an example with unknown attribute value is replaced by a set of weighted examples corresponding to the probability distribution of the missing value. The typical consequences of noise in learning data are low prediction accuracy of a learned model in new data and complex models that are hard to interpret and understand for the user.