Intro to Probability for Business
Backward elimination is a model selection technique used to refine a statistical model by systematically removing the least significant variables. This process starts with a full model that includes all potential predictors and iteratively eliminates the variables that do not contribute meaningfully to the model's predictive power. The goal is to simplify the model while retaining its accuracy, making it more interpretable and efficient in terms of computation.
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