MCQs

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Overall MCQs for Reference

1. Which method evaluates the statistical relevance of features independently of any model?
   a. Wrapper
   b. Embedded
   c. Filter
   d. Hybrid
   Answer: c. Filtergeeksforgeeks+2​
2. What is a major advantage of filter methods in high-dimensional datasets?
   a. High computational cost
   b. Less bias from classifier
   c. Fine-tuned for classifier
   d. Increased overfitting
   Answer: b. Less bias from classifierpmc.ncbi.nlm.nih+1​
3. Which statistical measure is used in variance thresholding for feature selection?
   a. Mean
   b. Variance
   c. Median
   d. Mode
   Answer: b. Variancegeeksforgeeks​
4. What is the key purpose of configuring feature filters in ML models?
   a. Data augmentation
   b. Selecting relevant features
   c. Increasing redundancy
   d. Reducing model accuracy
   Answer: b. Selecting relevant featuresalmabetter+1​
5. Filtering out features with Pearson correlation below a threshold removes?
   a. Highly correlated features
   b. Weakly informative features
   c. All target features
   d. Highly redundant features
   Answer: b. Weakly informative featurespmc.ncbi.nlm.nih+1​
6. Which filter method evaluates each feature independently using metrics such as chi-square?
   a. Wrapper
   b. Univariate filter
   c. Multivariate filter
   d. Embedded
   Answer: b. Univariate filtergeeksforgeeks+1​
7. What does filter-based feature selection ignore compared to embedded methods?
   a. Feature ranking
   b. Classifier interaction
   c. Data sampling
   d. Irrelevant features
   Answer: b. Classifier interactionstratascratch+1​
8. Which filter method aims to find both relevance and minimal redundancy?
   a. Chi-square test
   b. Correlation coefficient
   c. mRMR
   d. ReliefF
   Answer: c. mRMRpmc.ncbi.nlm.nih​
9. Which metric is commonly used to filter categorical variables?
   a. Variance
   b. Information gain
   c. Pearson correlation
   d. Euclidean distance
   Answer: b. Information gainpmc.ncbi.nlm.nih​
10. What is the result of using too many irrelevant features in filters?
    a. Decreased model accuracy
    b. Improved training time
    c. Increased feature importance
    d. Reduced data loss
    Answer: a. Decreased model accuracyalmabetter+1​
11. Mutual information filter methods are suited for detecting:
    a. Classifier bias
    b. Nonlinear feature interactions
    c. Overfitting
    d. Model selection
    Answer: b. Nonlinear feature interactionspmc.ncbi.nlm.nih​
12. Which feature selection method is independent of the chosen classifier?
    a. Filter
    b. Embedded
    c. Wrapper
    d. Ensemble
    Answer: a. Filtergeeksforgeeks+1​
13. Relief algorithm is designed to:
    a. Perform backward selection
    b. Detect feature redundancy
    c. Score features by differentiating classes
    d. Impose classifier constraints
    Answer: c. Score features by differentiating classespmc.ncbi.nlm.nih​
14. When configuring filter settings, what is the primary goal?
    a. Filter all features
    b. Retain only relevant features
    c. Sort features by name
    d. Randomize feature order
    Answer: b. Retain only relevant featuresastera+1​
15. Which filter technique assesses the correlation between feature and target variable?
    a. PCA
    b. Pearson coefficient
    c. SVM
    d. Ensemble learning
    Answer: b. Pearson coefficientgeeksforgeeks​
16. What does a high absolute correlation coefficient indicate?
    a. Low feature relevance
    b. Strong linear relationship
    c. Irrelevant feature
    d. Poor model fit
    Answer: b. Strong linear relationshipgeeksforgeeks​
17. Which filter removes features with extremely low variance?
    a. Forward selection
    b. Variance thresholding
    c. Backward elimination
    d. Random forests
    Answer: b. Variance thresholdinggeeksforgeeks​
18. Wrapper methods differ from filter methods because they:
    a. Use model performance for feature selection
    b. Ignore target variable
    c. Require more computational resources
    d. Select features randomly
    Answer: a. Use model performance for feature selectionpmc.ncbi.nlm.nih+1​
19. The primary advantage of filter methods for feature selection is:
    a. Highly specific models
    b. Scalability and simplicity
    c. High computational cost
    d. Data overfitting
    Answer: b. Scalability and simplicitygeeksforgeeks+1​
20. What iterative process is recommend for refining filter settings?
    a. Gradient descent
    b. Validation and adjustment
    c. Boosting
    d. Data imputation
    Answer: b. Validation and adjustmentastera​
21. Which filter method is used to detect pairwise feature interactions?
    a. Relief
    b. mRMR
    c. Chi-square test
    d. SVM
    Answer: a. Reliefpmc.ncbi.nlm.nih​
22. What best describes filter-based approaches in feature selection?
    a. Algorithm-specific evaluation
    b. Model-free statistical analysis
    c. Data augmentation methods
    d. Neural network integration
    Answer: b. Model-free statistical analysisstratascratch+1​
23. Multivariate filter methods consider features:
    a. Individually
    b. Simultaneously
    c. Sequentially
    d. Randomly
    Answer: b. Simultaneouslypmc.ncbi.nlm.nih​
24. Which filter method evaluates feature relevance without considering redundancy?
    a. Univariate filter
    b. Multivariate filter
    c. Wrapper method
    d. Embedded method
    Answer: a. Univariate filterpmc.ncbi.nlm.nih​
25. Conditional Mutual Information Maximization filter detects:
    a. Class imbalance
    b. Feature interaction
    c. Data augmentation
    d. Model regularization
    Answer: b. Feature interactionpmc.ncbi.nlm.nih​
26. Which filter selection technique is based on ranking features using information gain?
    a. Embedded
    b. Wrapper
    c. Filter
    d. Ensemble
    Answer: c. Filteralmabetter+1​
27. What is the function of variance threshold in feature selection?
    a. Select features with high variance
    b. Select features with low mean
    c. Normalize selected features
    d. Impute missing features
    Answer: a. Select features with high variancegeeksforgeeks​
28. Redundant features in filter methods can:
    a. Increase model interpretability
    b. Decrease model performance
    c. Reduce computational cost
    d. Improve feature ranking
    Answer: b. Decrease model performancepmc.ncbi.nlm.nih​
29. Which technique detects and removes feature redundancy in multivariate filters?
    a. mRMR
    b. Chi-square
    c. Univariate filter
    d. PCA
    Answer: a. mRMRpmc.ncbi.nlm.nih​
30. Filter-based feature selection can easily scale to:
    a. Small datasets only
    b. High-dimensional datasets
    c. Binary classification only
    d. Ensemble learning tasks
    Answer: b. High-dimensional datasetsgeeksforgeeks+1​
31. Which filter method uses scores from statistical tests?
    a. Relief
    b. Univariate
    c. Wrapper
    d. Embedded
    Answer: b. Univariatepmc.ncbi.nlm.nih​
32. Exhaustive search for feature interactions is possible with which algorithm family?
    a. TEAM
    b. KNN
    c. PCA
    d. AdaBoost
    Answer: a. TEAMpmc.ncbi.nlm.nih​
33. In feature selection, a threshold parameter is primarily used to:
    a. Set minimum feature rank
    b. Exclude irrelevant features
    c. Enhance feature redundancy
    d. Ignore valid data
    Answer: b. Exclude irrelevant featuresgeeksforgeeks​
34. What is the main outcome of filtering out irrelevant features in ML?
    a. Longer training times
    b. Better model accuracy
    c. Increased overfitting
    d. Reduced feature diversity
    Answer: b. Better model accuracyalmabetter+1​
35. Which filter family is specifically designed to recognize pairwise interactions?
    a. Relief-based
    b. Wrapper-based
    c. Embedded-based
    d. Ensemble-based
    Answer: a. Relief-basedpmc.ncbi.nlm.nih​
36. Which filter algorithm minimizes both relevance and redundancy?
    a. Random forest
    b. mRMR
    c. Chi-square
    d. KMeans
    Answer: b. mRMRpmc.ncbi.nlm.nih​
37. Features are ranked based on their:
    a. Scores from statistical tests
    b. Random assignment
    c. Individual classifier performance
    d. Training time
    Answer: a. Scores from statistical testspmc.ncbi.nlm.nih​
38. What type of filter method uses the Mann-Whitney U test?
    a. Multivariate
    b. Univariate
    c. Wrapper
    d. Embedded
    Answer: b. Univariatepmc.ncbi.nlm.nih​
39. Information gain filter is most effective with:
    a. Categorical variables
    b. Continuous features
    c. Image data
    d. Time series
    Answer: a. Categorical variablespmc.ncbi.nlm.nih​
40. More complex filter algorithms enable detection of:
    a. Only single feature relevance
    b. Feature interactions and redundancy
    c. Data imputation accuracy
    d. Ensemble stability
    Answer: b. Feature interactions and redundancypmc.ncbi.nlm.nih​
41. Pearson’s correlation in feature filters assesses:
    a. Nonlinear dependence only
    b. Linear dependence
    c. Feature redundancy
    d. Data normalization
    Answer: b. Linear dependencegeeksforgeeks​
42. Redundant features can:
    a. Be ignored by filters
    b. Increase model complexity unnecessarily
    c. Improve filter accuracy
    d. Always increase predictive power
    Answer: b. Increase model complexity unnecessarilygeeksforgeeks+1​
43. Which filter method is not suitable to capture feature interactions?
    a. Univariate
    b. Mutual Information
    c. ReliefF
    d. TEAM
    Answer: a. Univariatepmc.ncbi.nlm.nih​
44. What is the main computational benefit of filter methods?
    a. Require model training
    b. Fast and independent of models
    c. Require deep learning systems
    d. Depend on ensemble methods
    Answer: b. Fast and independent of modelsgeeksforgeeks+1​
45. Which filter algorithm is best for genetics datasets with thousands of features?
    a. mRMR
    b. TEAM
    c. Chi-square
    d. Relief
    Answer: a. mRMRpmc.ncbi.nlm.nih​
46. Feature filters in ML are designed to:
    a. Adjust model hyperparameters
    b. Reduce the number of features
    c. Guarantee top model accuracy
    d. Replace all variable names
    Answer: b. Reduce the number of featuresbrainalystacademy+1​
47. What is the outcome if filters are not validated after configuration?
    a. Improved model accuracy
    b. Poor insights and errors
    c. More feature diversity
    d. Better performance
    Answer: b. Poor insights and errorsastera​
48. Selecting all variables as features leads to:
    a. Higher model interpretability
    b. Lower accuracy than logical selection
    c. Zero bias
    d. Minimum computation
    Answer: b. Lower accuracy than logical selectionalmabetter​
49. Main distinction of filter methods is their:
    a. Dependency on model
    b. Independence from model training
    c. Direct integration with deep learning
    d. Use of reinforcement learning
    Answer: b. Independence from model trainingstratascratch+1​
50. In machine learning, configuring filter settings can lead to which benefit?
    a. More irrelevant features
    b. Efficient and accurate models
    c. Increased training time
    d. Reduced interpretability
    Answer: b. Efficient and accurate modelsastera+2​

1. https://www.geeksforgeeks.org/machine-learning/feature-selection-filter-methods/
2. https://pmc.ncbi.nlm.nih.gov/articles/PMC9580915/
3. https://www.almabetter.com/bytes/articles/filter-methods-for-feature-selection-in-machine-learning
4. https://www.stratascratch.com/blog/feature-selection-techniques-in-machine-learning/
5. https://www.astera.com/type/blog/data-filtering/
6. https://brainalystacademy.com/filter-methods/
7. https://www.sanfoundry.com/1000-machine-learning-questions-answers/
8. https://www.scribd.com/document/754714735/T1-machine-learning-mcq-questions-and-answers-key
9. https://www.gyansetu.in/blog/machine-learning-mcq-interview-preparation/
10. https://www.studocu.com/in/document/shivaji-university/computer-science/machine-learning-mcq-questions-and-answers/29821794
11. https://interviewkickstart.com/blogs/articles/machine-learning-mcqs-answers
12. https://www.interviewbit.com/machine-learning-mcq/
13. https://www.geeksforgeeks.org/machine-learning/feature-selection-techniques-in-machine-learning/
14. https://futureskillsacademy.com/blog/top-machine-learning-mcqs-with-answers/
15. https://www.cliffsnotes.com/study-notes/26856187
16. https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/filter-based-feature-selection?view=azureml-api-2
17. https://www.diva-portal.org/smash/get/diva2:1371211/FULLTEXT01.pdf
18. https://www.freetimelearning.com/online-quiz/machine-learning-quiz
19. https://opendl.ifip-tc6.org/db/conf/ideal/ideal2007/Sanchez-MaronoAT07.pdf
20. https://www.sciencedirect.com/science/article/pii/S016794731930194X