ISYE 6501 Final Quiz | Questions and Answers |
2025 Update | 100% Correct
is the primary purpose of cross-validation?**
A: To estimate the predictive performance of a model on unseen data and prevent overfitting.
**2. Q: In regression, what does a high R-squared value indicate?**
A: That a large proportion of the variance in the dependent variable is predictable from the independent
variables.
**3. Q: What is the "Curse of Dimensionality"?**
A: As the number of dimensions (features) increases, the amount of data required to generalize
accurately grows exponentially.
**4. Q: How does Lasso (L1) regression differ from Ridge (L2) regression?**
A: Lasso can shrink coefficients to exactly zero, effectively performing feature selection, while Ridge
shrinks them toward zero but rarely to zero.
**5. Q: What is the main assumption of Linear Regression regarding error terms?**
A: Errors are normally distributed with a mean of zero and constant variance (homoscedasticity).
**6. Q: Why do we normalize or scale data before using K-Nearest Neighbors (KNN)?**
A: Because KNN is distance-based; features with larger scales will disproportionately influence the
distance calculation.
**7. Q: Define "Sensitivity" (Recall) in a confusion matrix.**
A: The ratio of True Positives to the total number of actual positive cases (TP / (TP + FN)).
2025 Update | 100% Correct
is the primary purpose of cross-validation?**
A: To estimate the predictive performance of a model on unseen data and prevent overfitting.
**2. Q: In regression, what does a high R-squared value indicate?**
A: That a large proportion of the variance in the dependent variable is predictable from the independent
variables.
**3. Q: What is the "Curse of Dimensionality"?**
A: As the number of dimensions (features) increases, the amount of data required to generalize
accurately grows exponentially.
**4. Q: How does Lasso (L1) regression differ from Ridge (L2) regression?**
A: Lasso can shrink coefficients to exactly zero, effectively performing feature selection, while Ridge
shrinks them toward zero but rarely to zero.
**5. Q: What is the main assumption of Linear Regression regarding error terms?**
A: Errors are normally distributed with a mean of zero and constant variance (homoscedasticity).
**6. Q: Why do we normalize or scale data before using K-Nearest Neighbors (KNN)?**
A: Because KNN is distance-based; features with larger scales will disproportionately influence the
distance calculation.
**7. Q: Define "Sensitivity" (Recall) in a confusion matrix.**
A: The ratio of True Positives to the total number of actual positive cases (TP / (TP + FN)).
