Prediction Regression MCQ Questions with Answers (Latest 2026)

Q1. Which option best describes linear regression?

A. Decision tree. B. Fit y = X w + b minimizing MSE. C. Logistic regression. D. K-means.

Select an answer to check.

Answer: Fit y = X w + b minimizing MSE.

Here, Fit y = X w + b minimizing MSE. is the right choice. Simplest regression baseline. It aligns directly with what the question asks about which option best describes linear regression. A quick elimination of partially true options helps confirm it.

Q2. What is the primary purpose of linear regression?

A. Logistic regression. B. K-means. C. Decision tree. D. Fit y = X w + b minimizing MSE.

Select an answer to check.

Answer: Fit y = X w + b minimizing MSE.

In this case, Fit y = X w + b minimizing MSE. is correct. Simplest regression baseline. It aligns directly with what the question asks about what is the primary purpose of linear regression. A quick elimination of partially true options helps confirm it.

Q3. Which statement about linear regression is most accurate?

A. Fit y = X w + b minimizing MSE. B. K-means. C. Logistic regression. D. Decision tree.

Select an answer to check.

Answer: Fit y = X w + b minimizing MSE.

The best option here is Fit y = X w + b minimizing MSE.. Simplest regression baseline. It aligns directly with what the question asks about which statement about linear regression is most accurate. A quick elimination of partially true options helps confirm it.

Q4. How is linear regression best characterized?

A. Decision tree. B. Fit y = X w + b minimizing MSE. C. K-means. D. Logistic regression.

Select an answer to check.

Answer: Fit y = X w + b minimizing MSE.

For this question, Fit y = X w + b minimizing MSE. is correct. Simplest regression baseline. It aligns directly with what the question asks about how is linear regression best characterized. A quick elimination of partially true options helps confirm it.

Q5. Which option best describes ridge regression?

A. Logistic. B. Linear regression with L2 penalty. C. Elastic net. D. Lasso.

Select an answer to check.

Answer: Linear regression with L2 penalty.

Linear regression with L2 penalty. is the correct answer here. Shrinks coefficients. It aligns directly with what the question asks about which option best describes ridge regression. A quick elimination of partially true options helps confirm it.

Q6. What is the primary purpose of ridge regression?

A. Lasso. B. Logistic. C. Linear regression with L2 penalty. D. Elastic net.

Select an answer to check.

Answer: Linear regression with L2 penalty.

Here, Linear regression with L2 penalty. is the right choice. Shrinks coefficients. This matches the core idea being tested around what is the primary purpose of ridge regression. A quick elimination of partially true options helps confirm it.

Q7. Which statement about ridge regression is most accurate?

A. Elastic net. B. Logistic. C. Linear regression with L2 penalty. D. Lasso.

Select an answer to check.

Answer: Linear regression with L2 penalty.

In this case, Linear regression with L2 penalty. is correct. Shrinks coefficients. This matches the core idea being tested around which statement about ridge regression is most accurate. A quick elimination of partially true options helps confirm it.

Q8. How is ridge regression best characterized?

A. Logistic. B. Linear regression with L2 penalty. C. Lasso. D. Elastic net.

Select an answer to check.

Answer: Linear regression with L2 penalty.

The best option here is Linear regression with L2 penalty.. Shrinks coefficients. This matches the core idea being tested around how is ridge regression best characterized. A quick elimination of partially true options helps confirm it.

Q9. Which option best describes lasso regression?

A. Linear regression with L1 penalty. B. Elastic net. C. Ridge. D. Logistic.

Select an answer to check.

Answer: Linear regression with L1 penalty.

For this question, Linear regression with L1 penalty. is correct. Performs feature selection. This matches the core idea being tested around which option best describes lasso regression. A quick elimination of partially true options helps confirm it.

Q10. What is the primary purpose of lasso regression?

A. Ridge. B. Logistic. C. Linear regression with L1 penalty. D. Elastic net.

Select an answer to check.

Answer: Linear regression with L1 penalty.

Linear regression with L1 penalty. is the correct answer here. Performs feature selection. This matches the core idea being tested around what is the primary purpose of lasso regression. A quick elimination of partially true options helps confirm it.

Q11. Which statement about lasso regression is most accurate?

A. Elastic net. B. Logistic. C. Linear regression with L1 penalty. D. Ridge.

Select an answer to check.

Answer: Linear regression with L1 penalty.

Here, Linear regression with L1 penalty. is the right choice. Performs feature selection. That is exactly the concept behind which statement about lasso regression is most accurate in this context. A quick elimination of partially true options helps confirm it.

Q12. How is lasso regression best characterized?

A. Elastic net. B. Logistic. C. Linear regression with L1 penalty. D. Ridge.

Select an answer to check.

Answer: Linear regression with L1 penalty.

In this case, Linear regression with L1 penalty. is correct. Performs feature selection. That is exactly the concept behind how is lasso regression best characterized in this context. A quick elimination of partially true options helps confirm it.

Q13. Which option best describes elastic net?

A. Ridge. B. Combines L1 and L2 penalties. C. Lasso. D. Logistic.

Select an answer to check.

Answer: Combines L1 and L2 penalties.

The best option here is Combines L1 and L2 penalties.. Mix of selection and shrinkage. That is exactly the concept behind which option best describes elastic net in this context. A quick elimination of partially true options helps confirm it.

Q14. What is the primary purpose of elastic net?

A. Logistic. B. Lasso. C. Ridge. D. Combines L1 and L2 penalties.

Select an answer to check.

Answer: Combines L1 and L2 penalties.

For this question, Combines L1 and L2 penalties. is correct. Mix of selection and shrinkage. That is exactly the concept behind what is the primary purpose of elastic net in this context. A quick elimination of partially true options helps confirm it.

Q15. Which statement about elastic net is most accurate?

A. Combines L1 and L2 penalties. B. Logistic. C. Ridge. D. Lasso.

Select an answer to check.

Answer: Combines L1 and L2 penalties.

Combines L1 and L2 penalties. is the correct answer here. Mix of selection and shrinkage. That is exactly the concept behind which statement about elastic net is most accurate in this context. A quick elimination of partially true options helps confirm it.

Q16. How is elastic net best characterized?

A. Logistic. B. Lasso. C. Ridge. D. Combines L1 and L2 penalties.

Select an answer to check.

Answer: Combines L1 and L2 penalties.

Here, Combines L1 and L2 penalties. is the right choice. Mix of selection and shrinkage. It fits the requirement in the prompt about how is elastic net best characterized. A quick elimination of partially true options helps confirm it.

Q17. Which option best describes polynomial regression?

A. Decision tree. B. Linear regression on polynomial features. C. Pure linear regression. D. K-means.

Select an answer to check.

Answer: Linear regression on polynomial features.

In this case, Linear regression on polynomial features. is correct. Beware overfitting at high degree. It fits the requirement in the prompt about which option best describes polynomial regression. A quick elimination of partially true options helps confirm it.

Q18. What is the primary purpose of polynomial regression?

A. K-means. B. Pure linear regression. C. Decision tree. D. Linear regression on polynomial features.

Select an answer to check.

Answer: Linear regression on polynomial features.

The best option here is Linear regression on polynomial features.. Beware overfitting at high degree. It fits the requirement in the prompt about what is the primary purpose of polynomial regression. A quick elimination of partially true options helps confirm it.

Q19. Which statement about polynomial regression is most accurate?

A. K-means. B. Decision tree. C. Linear regression on polynomial features. D. Pure linear regression.

Select an answer to check.

Answer: Linear regression on polynomial features.

For this question, Linear regression on polynomial features. is correct. Beware overfitting at high degree. It fits the requirement in the prompt about which statement about polynomial regression is most accurate. A quick elimination of partially true options helps confirm it.

Q20. How is polynomial regression best characterized?

A. Decision tree. B. Linear regression on polynomial features. C. Pure linear regression. D. K-means.

Select an answer to check.

Answer: Linear regression on polynomial features.

Linear regression on polynomial features. is the correct answer here. Beware overfitting at high degree. It fits the requirement in the prompt about how is polynomial regression best characterized. A quick elimination of partially true options helps confirm it.

Q21. Which option best describes decision tree regressor?

A. Logistic regression. B. Linear regression. C. Tree predicting numeric values. D. K-means.

Select an answer to check.

Answer: Tree predicting numeric values.

Here, Tree predicting numeric values. is the right choice. Captures non-linearity. This is the most accurate statement for which option best describes decision tree regressor. A quick elimination of partially true options helps confirm it.

Q22. What is the primary purpose of decision tree regressor?

A. K-means. B. Logistic regression. C. Linear regression. D. Tree predicting numeric values.

Select an answer to check.

Answer: Tree predicting numeric values.

In this case, Tree predicting numeric values. is correct. Captures non-linearity. This is the most accurate statement for what is the primary purpose of decision tree. A quick elimination of partially true options helps confirm it.

Q23. Which statement about decision tree regressor is most accurate?

A. Logistic regression. B. K-means. C. Tree predicting numeric values. D. Linear regression.

Select an answer to check.

Answer: Tree predicting numeric values.

The best option here is Tree predicting numeric values.. Captures non-linearity. This is the most accurate statement for which statement about decision tree regressor is most. A quick elimination of partially true options helps confirm it.

Q24. How is decision tree regressor best characterized?

A. Tree predicting numeric values. B. Linear regression. C. Logistic regression. D. K-means.

Select an answer to check.

Answer: Tree predicting numeric values.

For this question, Tree predicting numeric values. is correct. Captures non-linearity. This is the most accurate statement for how is decision tree regressor best characterized. A quick elimination of partially true options helps confirm it.

Q25. Which option best describes random forest regressor?

A. K-means. B. Single tree. C. Boosting only. D. Bagging of regression trees.

Select an answer to check.

Answer: Bagging of regression trees.

Bagging of regression trees. is the correct answer here. Robust default. This is the most accurate statement for which option best describes random forest regressor. A quick elimination of partially true options helps confirm it.

Q26. What is the primary purpose of random forest regressor?

A. Single tree. B. Boosting only. C. K-means. D. Bagging of regression trees.

Select an answer to check.

Answer: Bagging of regression trees.

Here, Bagging of regression trees. is the right choice. Robust default. It aligns directly with what the question asks about what is the primary purpose of random forest. The other options are either incomplete or contextually incorrect.

Q27. Which statement about random forest regressor is most accurate?

A. Bagging of regression trees. B. Boosting only. C. Single tree. D. K-means.

Select an answer to check.

Answer: Bagging of regression trees.

In this case, Bagging of regression trees. is correct. Robust default. It aligns directly with what the question asks about which statement about random forest regressor is most. The other options are either incomplete or contextually incorrect.

Q28. How is random forest regressor best characterized?

A. Bagging of regression trees. B. K-means. C. Boosting only. D. Single tree.

Select an answer to check.

Answer: Bagging of regression trees.

The best option here is Bagging of regression trees.. Robust default. It aligns directly with what the question asks about how is random forest regressor best characterized. The other options are either incomplete or contextually incorrect.

Q29. Which option best describes gradient boosting regressor?

A. Bagging only. B. K-means. C. Linear model. D. Boosted tree regression.

Select an answer to check.

Answer: Boosted tree regression.

For this question, Boosted tree regression. is correct. Often strong on tabular. It aligns directly with what the question asks about which option best describes gradient boosting regressor. The other options are either incomplete or contextually incorrect.

Q30. What is the primary purpose of gradient boosting regressor?

A. Linear model. B. K-means. C. Boosted tree regression. D. Bagging only.

Select an answer to check.

Answer: Boosted tree regression.

Boosted tree regression. is the correct answer here. Often strong on tabular. It aligns directly with what the question asks about what is the primary purpose of gradient boosting. The other options are either incomplete or contextually incorrect.

Q31. Which statement about gradient boosting regressor is most accurate?

A. Linear model. B. K-means. C. Boosted tree regression. D. Bagging only.

Select an answer to check.

Answer: Boosted tree regression.

Here, Boosted tree regression. is the right choice. Often strong on tabular. This matches the core idea being tested around which statement about gradient boosting regressor is most. The other options are either incomplete or contextually incorrect.

Q32. How is gradient boosting regressor best characterized?

A. Boosted tree regression. B. Linear model. C. Bagging only. D. K-means.

Select an answer to check.

Answer: Boosted tree regression.

In this case, Boosted tree regression. is correct. Often strong on tabular. This matches the core idea being tested around how is gradient boosting regressor best characterized. The other options are either incomplete or contextually incorrect.

Q33. Which option best describes k-NN regressor?

A. Logistic. B. Tree. C. Linear model. D. Average of k nearest neighbors.

Select an answer to check.

Answer: Average of k nearest neighbors.

The best option here is Average of k nearest neighbors.. Lazy learner. This matches the core idea being tested around which option best describes k-nn regressor. The other options are either incomplete or contextually incorrect.

Q34. What is the primary purpose of k-NN regressor?

A. Average of k nearest neighbors. B. Logistic. C. Linear model. D. Tree.

Select an answer to check.

Answer: Average of k nearest neighbors.

For this question, Average of k nearest neighbors. is correct. Lazy learner. This matches the core idea being tested around what is the primary purpose of k-nn regressor. The other options are either incomplete or contextually incorrect.

Q35. Which statement about k-NN regressor is most accurate?

A. Tree. B. Linear model. C. Logistic. D. Average of k nearest neighbors.

Select an answer to check.

Answer: Average of k nearest neighbors.

Average of k nearest neighbors. is the correct answer here. Lazy learner. This matches the core idea being tested around which statement about k-nn regressor is most accurate. The other options are either incomplete or contextually incorrect.

Q36. How is k-NN regressor best characterized?

A. Logistic. B. Average of k nearest neighbors. C. Tree. D. Linear model.

Select an answer to check.

Answer: Average of k nearest neighbors.

Here, Average of k nearest neighbors. is the right choice. Lazy learner. That is exactly the concept behind how is k-nn regressor best characterized in this context. The other options are either incomplete or contextually incorrect.

Q37. Which option best describes MSE?

A. Mean squared error loss. B. MAE. C. Huber loss. D. Hinge loss.

Select an answer to check.

Answer: Mean squared error loss.

In this case, Mean squared error loss. is correct. Penalizes large errors. That is exactly the concept behind which option best describes mse in this context. The other options are either incomplete or contextually incorrect.

Q38. What is the primary purpose of MSE?

A. Mean squared error loss. B. Huber loss. C. Hinge loss. D. MAE.

Select an answer to check.

Answer: Mean squared error loss.

The best option here is Mean squared error loss.. Penalizes large errors. That is exactly the concept behind what is the primary purpose of mse in this context. The other options are either incomplete or contextually incorrect.

Q39. Which statement about MSE is most accurate?

A. Hinge loss. B. Mean squared error loss. C. MAE. D. Huber loss.

Select an answer to check.

Answer: Mean squared error loss.

For this question, Mean squared error loss. is correct. Penalizes large errors. That is exactly the concept behind which statement about mse is most accurate in this context. The other options are either incomplete or contextually incorrect.

Q40. How is MSE best characterized?

A. Hinge loss. B. Huber loss. C. Mean squared error loss. D. MAE.

Select an answer to check.

Answer: Mean squared error loss.

Mean squared error loss. is the correct answer here. Penalizes large errors. That is exactly the concept behind how is mse best characterized in this context. The other options are either incomplete or contextually incorrect.

Q41. Which option best describes RMSE?

A. Square root of MSE. B. MAE. C. MAPE. D. R^2.

Select an answer to check.

Answer: Square root of MSE.

Here, Square root of MSE. is the right choice. Same units as target. It fits the requirement in the prompt about which option best describes rmse. The other options are either incomplete or contextually incorrect.

Q42. What is the primary purpose of RMSE?

A. Square root of MSE. B. R^2. C. MAPE. D. MAE.

Select an answer to check.

Answer: Square root of MSE.

In this case, Square root of MSE. is correct. Same units as target. It fits the requirement in the prompt about what is the primary purpose of rmse. The other options are either incomplete or contextually incorrect.

Q43. Which statement about RMSE is most accurate?

A. MAE. B. MAPE. C. R^2. D. Square root of MSE.

Select an answer to check.

Answer: Square root of MSE.

The best option here is Square root of MSE.. Same units as target. It fits the requirement in the prompt about which statement about rmse is most accurate. The other options are either incomplete or contextually incorrect.

Q44. How is RMSE best characterized?

A. MAE. B. R^2. C. Square root of MSE. D. MAPE.

Select an answer to check.

Answer: Square root of MSE.

For this question, Square root of MSE. is correct. Same units as target. It fits the requirement in the prompt about how is rmse best characterized. The other options are either incomplete or contextually incorrect.

Q45. Which option best describes MAE?

A. Mean absolute error. B. R^2. C. MAPE. D. MSE.

Select an answer to check.

Answer: Mean absolute error.

Mean absolute error. is the correct answer here. Robust to outliers. It fits the requirement in the prompt about which option best describes mae. The other options are either incomplete or contextually incorrect.

Q46. What is the primary purpose of MAE?

A. Mean absolute error. B. MSE. C. MAPE. D. R^2.

Select an answer to check.

Answer: Mean absolute error.

Here, Mean absolute error. is the right choice. Robust to outliers. This is the most accurate statement for what is the primary purpose of mae. The other options are either incomplete or contextually incorrect.

Q47. Which statement about MAE is most accurate?

A. MSE. B. R^2. C. MAPE. D. Mean absolute error.

Select an answer to check.

Answer: Mean absolute error.

In this case, Mean absolute error. is correct. Robust to outliers. This is the most accurate statement for which statement about mae is most accurate. The other options are either incomplete or contextually incorrect.

Q48. How is MAE best characterized?

A. R^2. B. MSE. C. Mean absolute error. D. MAPE.

Select an answer to check.

Answer: Mean absolute error.

The best option here is Mean absolute error.. Robust to outliers. This is the most accurate statement for how is mae best characterized. The other options are either incomplete or contextually incorrect.

Q49. Which option best describes MAPE?

A. RMSE. B. R^2. C. MSE. D. Mean absolute percentage error.

Select an answer to check.

Answer: Mean absolute percentage error.

For this question, Mean absolute percentage error. is correct. Beware near-zero targets. This is the most accurate statement for which option best describes mape. The other options are either incomplete or contextually incorrect.

Q50. What is the primary purpose of MAPE?

A. R^2. B. RMSE. C. Mean absolute percentage error. D. MSE.

Select an answer to check.

Answer: Mean absolute percentage error.

Mean absolute percentage error. is the correct answer here. Beware near-zero targets. This is the most accurate statement for what is the primary purpose of mape. The other options are either incomplete or contextually incorrect.