Variable Selection

Daniel P. McGibney
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Daniel P. McGibney: University of Miami, Management Science

Chapter Chapter 9 in Applied Linear Regression for Business Analytics with Python, 2026, pp 261-314 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: 2026
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DOI: 10.1007/978-3-032-23806-1_9

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