ISYE 6501 Midterm 2 Terms Exam questions and answers Graded A+ latest update Backward elimination Variable selection process that starts with all variables and then iteratively removes the least-immediately-relevant variables from the model. Elastic

ISYE 6501 Midterm 2 Terms Exam questions and answers Graded A+ latest update Backward elimination Variable selection process that starts with all variables and then iteratively removes the least-immediately-relevant variables from the model. Elastic net Combination of lasso and ridge regression. Forward selection Variable selection process that starts with no variables and then iteratively adds the most-immediately-relevant variables to the model. Lasso/Lasso regression Method for limiting the number of variables in a model by limiting the sum of all coefficients' absolute values. Can be very helpful when number of data points is less than number of factors. Overfitting Building a model that describes random effects instead of or in significant addition to the real effects; often caused by having too many factors or parameters compared to the numb