Modelling a target attribute by other attributes in the data is perhaps the most traditional data mining task. When there are many attributes in the data, one needs to know which of the attribute(s) are relevant for modelling the target, either as a group or the one feature that is most appropriate to select within the model construction process in progress. There are many approaches for selecting the attribute(s) in machine learning. We examine various important concepts and approaches that are used for this purpose and contrast their strengths. Discretization of numeric attributes is also discussed for its use is prevalent in many modelling techniques.

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