Feature Selection:
In predictive modeling, feature selection, also called variable selection, is the process (usually automated) of sorting through variables to retain variables that are likely to be informative in prediction, and discard or combine those that are redundant. “Features” is a term used by the machine learning community, sometimes used to refer to the individual variables being combined, and sometimes used to refer to the derived variables that result from that process. “Subset selection” methods, originally developed for regression models, are an important feature selection method.
This course introduces to the basic concepts in predictive analytics to visualize and explore data to understand the two core paradigms that account for most business applications of predictive modeling: classification and prediction.