Variable Selection

Daniel P. McGibney
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Daniel P. McGibney: University of Miami

Chapter Chapter 9 in Applied Linear Regression for Business Analytics with R, 2023, pp 221-265 from Springer

Abstract: Abstract While it may be a bit strict to say that all models are wrong, it is often the case that a model is imperfect. However, an imperfect model may still provide a great amount of value. When attempting to find the best model from the data given, being able to select the predictor variables is of utmost importance in the model building process. In fact, one of the most important aspects of model creation is knowing which predictor variables to use, a process sometimes called feature selection or variable selection. Variable selection can be tremendously helpful when an analyst is attempting to find a mathematical model that is relatively close to the true state.

Date: 2023
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DOI: 10.1007/978-3-031-21480-6_9

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