Machine Learning in Java - Second Edition

Data reduction

Data reduction deals with abundant attributes and instances. The number of attributes corresponds to the number of dimensions in our dataset. Dimensions with low prediction power contribute very little to the overall model, and cause a lot of harm. For instance, an attribute with random values can introduce some random patterns that will be picked up by a machine learning algorithm. It may happen that data contains a large number of missing values, wherein we have to find the reason for missing values in large numbers, and on that basis, it may fill it with some alternate value or impute or remove the attribute altogether. If 40% or more values are missing, then it may be advisable to remove such attributes, as this will impact ...