ISYE 6501 Final Quiz Questions and Answers 2026 Update 100 Correct GT Verified Latest Update
ISYE Update GT Verified Quiz | Complete Solutions Updated Reasons to reduce number of factors - answer - Avoid Overfitting Simplicity & Explainability Greedy Variable Selection Methods - answer - Forward Selection Backward Elimination Stepwise Regression Greedy Variable Selection - Definition - answer - Step-by step model evaluation methods to help find the best combination of variables for predictive models. Greedy Variable Selection - Pros - answer - Fast Good for initial exploration Greedy Variable Selection - Cons - answer - May overfit to random patterns May find locally good but globablly suboptimal solutions Often overfit more than global methods Forward Selection - answer - Greedy Build up from nothing, adds the best new factor at each step, stops when no factor improves the model enough Backward Elimination - answer - Greedy Narrow down from all variables, removes the worst factor at each step, stops when no factor is bad enough to remove Stepwise Regression - answer - Greedy Bidirectional, combines forward and backward: after each step, immediately re-check and remove any factors that are no longer useful Global Variable Selection Methods - answer - LASSO Regression Ridge Regression Elastic Net Global Variable Selection - Definition - answer - Methods that optimize over all factors simultaneously rather than step-by step. Lasso Regression - answer - Least Absolute Shrinkage and Selection Operator Variable Selection Adds constraint to linear regression that limits the total sum of absolute values of coefficients (high variance) Constraint region shape = diamond Lasso Formula - answer - Minimize SSE subject to sum(|aj|) =T Ridge Regression - answer - Limits the sum of squares by shrinking all coefficients towards 0, reduces overfitting by reducing model variance (high bias) Constraint Region shape = circle Ridge Regression Formula - answer - minimize SSE subject to sum(aj^2) =T Elastic Net - answer - Combines Lasso and ridge regressions. Useful when both benefits are needed, but also inherits drawbacks of each Elastic Net Formula - answer - Sum(|aj|) + sum(aj^2) T Bias - answer - Error from missing real patterns (underfitting) More bias = predictions cluster toward constant regardless of input Variance - answer - Error from fitting random noise (overfitting) More bias = predictions change dramatically with small data changes Bias-Variance Tradeoff - answer - Fundamental tradeoff between fitting real patterns and fitting random noise. Point in the middle where total prediction error (bias + variance) is minimized. Choosing Variable Selection Method - Initial Exploratory Analysis - answer - Greedy models Forward/backwards/stepwise regressions Fast, gives a starting point Choosing Variable Selection Method - Building a Production Model - answer - LASSO or Elastic Net Better predictive accuracy Choosing Variable Selection Method - Highly Correlated Predictors - answer - Check correlations manually Elastic Net or Ridge may help Choosing Variable Selection Method - Need Interpretable Model - answer - LASSO (fewer variables) Check practical effect sizes Choosing Variable Selection Method - Model Appears Overfit - answer - Ridge Regression to shrink coefficients Reduce Tau Ridge carefully Design of Experiments - answer - Designing a process for collecting the most useful subset of data quickly and efficiently Useful when collecting the full dataset is impossible or too slow Core DOE Concepts - answer - Comparison and Control Blocking Comparison and Control - answer - Controlling for all other important factors for comparing 2 or more groups. If factors are not controlled, differences may not be due to the focused factor. Blocking - answer - A variable that introduces variation but is not the focus of the study. Analyze blocks separately to reduce variance and get cleaner estimates A/B Testing - answer - Choosing the better of exactly 2 alternatives when you can collect data before maki
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