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Machine learning and. In machine learning and statistics, 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:
Feature selection with sparse data. If you use sparse data (i.e. data represented as sparse matrices), only will deal with the data without making it dense. Warning. Beware not to use a regression scoring function with a classification problem, you will get useless results.