MCQs

Disclaimer:

Educational Use Only:

The website links provided are to be used strictly for educational and knowledge-gaining purposes only.

No Misuse:

Do not misuse the links or the information contained therein for any commercial, illegal, or unethical activities.

Respect for Websites and Businesses:

Respect the intellectual property and business interests of the websites and their owners. Do not attempt to alter, copy, or redistribute content without proper authorization.

Acknowledge Value of Knowledge:

Recognize and value the knowledge and resources shared by these websites. Use the information responsibly and ethically.

No Endorsement:

The provision of these links does not imply endorsement of the linked sites, their content, or their products and services. Users are responsible for evaluating the suitability and accuracy of the information.

User Responsibility:

By accessing these links, you agree to use the information at your own risk and to comply with all applicable laws and website terms of use.

Overall MCQs for Reference

1. Linear regression belongs to which category?
   a. Reinforcement learning
   b. Unsupervised learning
   c. Supervised learning
   d. Semi-supervised learning
   Answer: c. Supervised learningsanfoundry+1​
2. What does regression model predict?
   a. Discrete class label
   b. Continuous value
   c. Probability
   d. Cluster group
   Answer: b. Continuous valueai-cbse​
3. Multiple linear regression models are used when:
   a. There is only one independent variable
   b. There are multiple independent variables
   c. The target is categorical
   d. The data is not numeric
   Answer: b. There are multiple independent variablesaionlinecourse+1​
4. Which regression method uses a penalty to reduce overfitting?
   a. Linear regression
   b. Logistic regression
   c. Ridge regression
   d. Naive Bayes
   Answer: c. Ridge regressionscribd​
5. Which regression model is best for binary classification?
   a. Linear regression
   b. Multiple regression
   c. Logistic regression
   d. Lasso regression
   Answer: c. Logistic regressionphdtalks​
6. Which metric is commonly used to evaluate regression models?
   a. Accuracy
   b. Precision
   c. Mean Squared Error
   d. Recall
   Answer: c. Mean Squared Errorigmguru​
7. Which regression method selects only relevant features?
   a. Lasso regression
   b. Ridge regression
   c. ElasticNet regression
   d. Principal Component Analysis
   Answer: a. Lasso regressionphdtalks​
8. What does the ‘Gradient Descent’ algorithm minimize in regression?
   a. Bias
   b. Prediction error
   c. Overfitting
   d. Complexity
   Answer: b. Prediction errorgeeksforgeeks​
9. Which is the equation for simple linear regression?
   a. 
   b. 
   c. 
   d. 
   Answer: a.  scribd+1​
10. Which is NOT an assumption of linear regression?
    a. Linearity
    b. Homoscedasticity
    c. Independence of errors
    d. Nonlinearity
    Answer: d. Nonlinearitygeeksforgeeks​
11. What is ‘heteroscedasticity’ in regression?
    a. Unequal error spread
    b. Perfect correlation
    c. Constant variance
    d. Nonlinear data
    Answer: a. Unequal error spreadgeeksforgeeks​
12. Which variable does regression try to predict?
    a. Independent
    b. Dependent
    c. Feature
    d. Covariate
    Answer: b. Dependentai-cbse​
13. In regression, what is a ‘residual’?
    a. Sum of errors
    b. Difference between observed and predicted value
    c. Slope of regression line
    d. Standard deviation
    Answer: b. Difference between observed and predicted valueinterviewkickstart​
14. Which method combines L1 and L2 regularization?
    a. Ridge regression
    b. Lasso regression
    c. ElasticNet regression
    d. Decision tree
    Answer: c. ElasticNet regressionphdtalks​
15. What happens if a regression model underfits?
    a. High bias
    b. High variance
    c. Low error
    d. Overfitting
    Answer: a. High biasinterviewkickstart​
16. What does R-squared measure in regression?
    a. Covariance
    b. Explained variance
    c. Confidence interval
    d. Prediction error
    Answer: b. Explained varianceigmguru​
17. Which regression technique can be used for predicting categorical outcomes?
    a. Polynomial regression
    b. Logistic regression
    c. Kernel regression
    d. Ridge regression
    Answer: b. Logistic regressionphdtalks​
18. Which regression technique creates interaction terms between variables?
    a. Linear regression
    b. Polynomial regression
    c. Ridge regression
    d. Bayesian regression
    Answer: b. Polynomial regressionphdtalks​
19. Which type of regression model can solve non-linear problems?
    a. Logistic regression
    b. Multiple regression
    c. Polynomial regression
    d. Simple linear regression
    Answer: c. Polynomial regressionphdtalks​
20. In regularization, what does increasing the penalty parameter do?
    a. Reduces model complexity
    b. Increases variance
    c. Decreases bias
    d. Adds more features
    Answer: a. Reduces model complexityscribd​
21. Which regression method handles multicollinearity well?
    a. Simple linear regression
    b. Lasso regression
    c. Ridge regression
    d. Decision trees
    Answer: c. Ridge regressionscribd​
22. What is the output of a regression model?
    a. Discrete class
    b. Continuous value
    c. Label
    d. Score
    Answer: b. Continuous valueaionlinecourse+1​
23. Root Mean Squared Error (RMSE) is:
    a. Square root of RSS
    b. Mean of RSS
    c. Square of bias
    d. Variance of outcome
    Answer: a. Square root of RSSgeeksforgeeks​
24. What is the loss function for linear regression?
    a. Cross-entropy
    b. Mean Squared Error
    c. Hinge loss
    d. Softmax
    Answer: b. Mean Squared Erroraionlinecourse​
25. Which regression model is suitable for estimating probabilities?
    a. Simple linear regression
    b. Logistic regression
    c. Polynomial regression
    d. Ridge regression
    Answer: b. Logistic regressionphdtalks​
26. What is regularization in regression?
    a. Adding more features
    b. Preventing overfitting by penalizing large weights
    c. Removing outliers
    d. Increasing training time
    Answer: b. Preventing overfitting by penalizing large weightsscribd​
27. What is the primary difference between linear and logistic regression?
    a. Linear predicts continuous outcomes, logistic predicts probability of category
    b. Both use similar equations
    c. Logistic works for time series
    d. Linear can’t handle more than one feature
    Answer: a. Linear predicts continuous outcomes, logistic predicts probability of categoryai-cbse+1​
28. Which regression approach works best when the data is linearly separable?
    a. Polynomial regression
    b. Logistic regression
    c. Linear regression
    d. Decision tree
    Answer: c. Linear regressionphdtalks​
29. What does overfitting lead to in regression?
    a. Low training error but high test error
    b. High bias
    c. Perfect generalization
    d. No variance
    Answer: a. Low training error but high test errorgyansetu+1​
30. Which algorithm is used to optimize coefficients in linear regression?
    a. Backpropagation
    b. Gradient Descent
    c. Random Forest
    d. Apriori
    Answer: b. Gradient Descentgeeksforgeeks​
31. In regression, which method is used to determine significance of input variables?
    a. t-test
    b. correlation analysis
    c. hypothesis testing
    d. all of these
    Answer: d. all of thesescribd​
32. Which library is commonly used to implement regression in Python?
    a. KMeans
    b. RandomForest
    c. LinearRegression
    d. GradientBoosting
    Answer: c. LinearRegressionplacementpreparation​
33. Which problem cannot be solved using regression models?
    a. Predicting house prices
    b. Predicting customer churn (yes/no)
    c. Predicting car mileage
    d. Predicting stock prices
    Answer: b. Predicting customer churn (yes/no)aionlinecourse+1​
34. Which is a key difference between supervised and unsupervised regression?
    a. Supervised regression uses labeled data
    b. Unsupervised regression uses labeled data
    c. Both use unlabeled data
    d. Both use labeled data
    Answer: a. Supervised regression uses labeled dataplacementpreparation​
35. In regression, what does coefficient of determination refer to?
    a. Variance
    b. Standard deviation
    c. R-squared
    d. Mean error
    Answer: c. R-squaredigmguru​
36. Which regression is robust to outliers?
    a. Ridge regression
    b. Lasso regression
    c. Huber regression
    d. Linear regression
    Answer: c. Huber regressionphdtalks​
37. For time series data, which regression method is commonly used?
    a. ARIMA
    b. Ridge regression
    c. Linear regression
    d. Logistic regression
    Answer: a. ARIMAphdtalks​
38. A regression model that uses non-linear kernel is:
    a. Polynomial regression
    b. Logistic regression
    c. Kernel regression
    d. Linear regression
    Answer: c. Kernel regressionphdtalks​
39. What is the main disadvantage of linear regression?
    a. Handles nonlinear data
    b. Sensitive to outliers
    c. Too complex
    d. Needs huge datasets
    Answer: b. Sensitive to outliersgeeksforgeeks+1​
40. Which regression method is preferred for high-dimensional data?
    a. Ridge regression
    b. Linear regression
    c. Multiple regression
    d. Lasso regression
    Answer: d. Lasso regressionstudocu+1​
41. Which variable type is predicted by regression models?
    a. Nominal
    b. Categorical
    c. Continuous
    d. String
    Answer: c. Continuousai-cbse​
42. Which model can handle more than two outcome variables?
    a. Binary logistic regression
    b. Multinomial logistic regression
    c. Ridge regression
    d. Linear regression
    Answer: b. Multinomial logistic regressionphdtalks​
43. What is cross-validation used for in regression?
    a. Tuning regularization
    b. Estimating model performance
    c. Increasing bias
    d. Feature selection
    Answer: b. Estimating model performancephdtalks​
44. Which is not a regression evaluation metric?
    a. Mean Absolute Error
    b. F1 Score
    c. R-squared
    d. RMSE
    Answer: b. F1 Scoreigmguru​
45. Which regression technique is most used for discrete dependent variables?
    a. Linear regression
    b. Ridge regression
    c. Logistic regression
    d. Lasso regression
    Answer: c. Logistic regressionphdtalks​
46. Which model assumes independence and linearity between variables?
    a. Polynomial regression
    b. Linear regression
    c. Ridge regression
    d. Logistic regression
    Answer: b. Linear regressionai-cbse+1​
47. What does adding more predictors to a regression model generally cause?
    a. Increases bias
    b. Increases variance
    c. Reduces complexity
    d. Improves interpretability
    Answer: b. Increases varianceinterviewkickstart+1​
48. Which concept helps prevent multicollinearity in regression?
    a. Feature scaling
    b. Regularization
    c. Random sampling
    d. Dimensionality reduction
    Answer: b. Regularizationscribd+1​
49. In regression analysis, which residual pattern indicates a model problem?
    a. Random scatter
    b. Funnel shape
    c. Horizontal line
    d. Uniform spread
    Answer: b. Funnel shapegeeksforgeeks​
50. Which regression type allows for qualitative dependent variables with more than two categories?
    a. Multinomial logistic regression
    b. Binary logistic regression
    c. Ridge regression
    d. Linear regression
    Answer: a. Multinomial logistic regressionphdtalks​regressiongeeksforgeeks​

1. https://www.sanfoundry.com/machine-learning-questions-answers-linear-regression/
2. https://www.studocu.vn/vn/document/truong-dai-hoc-fpt/machine-learning/test-1-with-key-10-3-its-about-machine-learning/87098432
3. https://www.ai-cbse.com/uncategorized/regression-in-ai/
4. https://www.aionlinecourse.com/ai-quiz-questions/machine-learning/regression
5. https://phdtalks.org/2021/07/mcq-on-linear-regression.html
6. https://www.scribd.com/document/660120451/ML-Unit-03-mcq
7. https://www.igmguru.com/blog/machine-learning-mcqs
8. https://www.geeksforgeeks.org/machine-learning/ml-linear-regression/
9. https://www.scribd.com/document/867988124/AI-Regression-Questions
10. https://interviewkickstart.com/blogs/articles/machine-learning-mcqs-answers
11. https://www.gyansetu.in/blog/machine-learning-mcq-interview-preparation/
12. https://www.placementpreparation.io/mcq/artificial-intelligence/page/3/
13. https://testbook.com/objective-questions/mcq-on-regression-analysis–5eea6a0b39140f30f369dd16
14. https://www.youtube.com/watch?v=0ERRfyB149I
15. https://futureskillsacademy.com/blog/top-machine-learning-mcqs-with-answers/
16. https://www.tutorialaicsip.com/mcq-artificial-intelligence-class-10/
17. https://testbook.com/objective-questions/mcq-on-linear-regression–5eea6a0b39140f30f369dd1c
18. https://www.interviewbit.com/artificial-intelligence-mcq/
19. https://www.iejme.com/download/linear-regression-model-to-predict-the-use-of-artificial-intelligence-in-experimental-science-15736.pdf
20. https://stem.elearning.unipd.it/mod/resource/view.php?id=242831