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Sometimes too much information can reduce the effectiveness of data mining. Some of the columns of data attributes assembled for building and testing a model may not contribute meaningful information to the model. Some may actually detract from the quality and accuracy of the model.
Irrelevant attributes add noise to the data and affect model accuracy. Noise increases the size of the model and the time and system resources needed for model building and scoring.
Feature Selection selects the most relevant attributes.
Feature Extraction combines attributes into a new reduced set of features Feature selection selects the most relevant attributes.