While these procedures have the advantage of being largely automated, they should be implemented with caution. There are several drawbacks of these approaches, such as the inflated type I error rate associated with multiple hypothesis testing and failure to properly address multicollinearity. Further, stepwise selection methods are computationally expensive to carry out and give a false pretense of automating the variable selection process despite providing no guarantee to achieve the best model or even one that is theoretically sound. Many modern machine learning algorithms have their own variable selection capabilities that don’t suffer from the pitfalls of these.
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