Feature selection is a term commonly used in data mining to
describe the tools and techniques available for reducing inputs to
a manageable size for processing and analysis. Feature selection
implies not only cardinality reduction, which means imposing an
arbitrary or predefined cutoff on the number of attributes that can
be considered when building a model, but also the choice of
attributes, meaning that either the analyst or the modeling tool
actively selects or discards attributes based on their usefulness
for analysis. "Feature selection methods best Practices" is the
mast reference that practitioners and researchers have long been
seeking. It is also the obvious choice for academic and research
scholars.
General
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