Regularization can be an indirect form of variable selection. When regularization makes coefficients very small, it is suggesting you drop those variables from the model entirely. There are many other forms of variable selection (also known as feature selection) out there. Some are direct i.e. they tell you explicitly to not include the variable in the model. Others are indirect, like regularization which will shrink the coefficients of unwanted variables to zero.
Please choose one of these variable selection methods and discuss it. Tell us what it does exactly. Discuss its pro’s and con’s versus other variable selection methods. Discuss a situation where you might apply this method. Then reply to two of your classmate’s posts with an actual example where it’s used and give a brief summary. Cite references. If it’s from the internet, a link to the analysis will suffice for citation purposes.
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