Advertisement
Published in:
2023 | OriginalPaper | Chapter
9. Variable Selection
Author : Daniel P. McGibney
Published in: Applied Linear Regression for Business Analytics with R
Publisher: Springer International Publishing
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
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.