5.1 - Ridge Regression | STAT 897D

It is not unusual to see the number of input variables greatly exceed the number of observations, e.g. micro-array data analysis, environmental pollution studies.

5.1 - Ridge Regression | STAT 897D Skip to Content Eberly College of Science STAT 897D Applied Data Mining and Statistical Learning Home » Lesson 5: Regression Shrinkage Methods 5.1 - Ridge Regression Printer-friendly version Motivation: too many predictors It is not unusual to see the number of input variables greatly exceed the number of observations, e.g. micro-array data analysis, environmental pollution studies. With many predictors, fitting the full model without penalization will result in large prediction intervals, and LS regression estimator may not uniquely exist. Motivation: ill-co

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