ISYE 6501 -Exam 2 Wks 8 – 12. Top Exam Questions and answers, 100% Accurate.

ISYE 6501 -Exam 2 Wks 8 – 12. Top Exam Questions and answers, 100% Accurate. Building simpler models with fewer factors helps avoid which problems? A. Overfitting B. Low prediction quality C. Bias in the most important factors D. Difficulty in interpretation - -A. Overfitting D. Difficulty of interpretation Two main reasons to limit # of factors in a model. - -1. Overfitting 2. Simplicity When is overfitting likely to happen? - -When the number of factors is close to the number of data points. How does using a # of factors that is close to the number of data points cause overfitting? - -The model too closely fits the random efffects. It fits that data set well, but fails to predict well on a new data set. Three reasons simple models are better than complex models. - -1. Less data is required 2. Less chance of including insignificant factors 3. Easier to interpret Which of these is a key difference between stepwise regression and lasso regression? A. Lasso regression requires the data to first be scaled B. Stepwise regression gives many models to choose from, while lasso gives just one. - -A. If the data isn't scaled, the coefficients can have artificially different orders of magnitude, which means they'll have unbalanced effects on the lasso constraint. Name three greedy approaches to variable selection. - -1. Forward Selection 2. Backward Elimination 3. Stepwise Regression Name two global approaches to variable selection. - -1. Lasso 2. Elastic Net What does a greedy approach to variable selection mean? - -At each step, the model does one thing that looks best without taking future options into consideration. A more classical approach.