In machine learning feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. Feature selection techniques are used for three reasons:
- simplification of models to make them easier to interpret by researchers/users
- shorter training times,
- enhanced generalization by reducing overfitting
The central premise when using a feature selection technique is that the data contains many features that are either redundant or irrelevant, and can thus be removed without incurring much loss of information. Redundant or irrelevant features are two distinct notions, since one relevant feature may be redundant in the presence of another relevant feature with which it is strongly correlated
Version 2 of EvalC3 will include an optimization procedure, which will help users find a specific mix of a given number of attributes that maximises the diversity of cases in a given data set.