Hands-On Data Warehousing with Azure Data Factory

Feature selection

In general, when we work with high-dimensional datasets, it is a good idea to reduce the number of features to only the most useful ones and discard the rest. This can lead to simpler models that generalize better. Feature selection is the process of reducing inputs for processing and analyzing or identifying the most significant features over the others. This selection of features is necessary to create a functional model so as to achieve a reduction in cardinality, imposing a limit greater than the number of features that must be considered during its creation.

These methods help us identify and remove redundant and irrelevant and therefore unnecessary features. In fact, in building a predictive model, such features do ...