MCQs

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Overall MCQs for Reference

1. Which type of algorithm is Random Forest?
   a. Supervised Learning
   b. Unsupervised Learning
   c. Reinforcement Learning
   d. Semi-supervised Learning
   Answer: a. Supervised Learninggeeksforgeeks+1​
2. Random Forest is primarily used for which of the following tasks?
   a. Classification
   b. Regression
   c. Both a and b
   d. Clustering
   Answer: c. Both a and bwikipedia+1​
3. What technique does Random Forest use to ensure model diversity?
   a. Cross-validation
   b. Bootstrapping
   c. Bagging
   d. Feature engineering
   Answer: b. Bootstrappinggeeksforgeeks+2​
4. In Random Forest, what is the main purpose of aggregating multiple decision trees?
   a. Reduce bias
   b. Reduce variance
   c. Increase complexity
   d. Slow down computations
   Answer: b. Reduce variancegeeksforgeeks+2​
5. What voting process does Random Forest use for classification?
   a. Weighted voting
   b. Majority voting
   c. Random selection
   d. Averaging
   Answer: b. Majority votinggithub+2​
6. What is the main aggregation method for regression in Random Forest?
   a. Mode
   b. Median
   c. Average
   d. Weighted sum
   Answer: c. Averagecareerfoundry+2​
7. How does Random Forest select features at each split in a tree?
   a. All features
   b. Most correlated features
   c. Random subset of features
   d. Features with missing values
   Answer: c. Random subset of featuresgeeksforgeeks+1​
8. What is an essential hyperparameter in Random Forest for feature selection?
   a. Number of classes
   b. Number of features to consider per split
   c. Distance metric
   d. Learning rate
   Answer: b. Number of features to consider per splitgeeksforgeeks+1​
9. What does increasing the number of trees in Random Forest generally do to model accuracy?
   a. Reduces accuracy
   b. Has no effect
   c. Improves stability and accuracy
   d. Always causes overfitting
   Answer: c. Improves stability and accuracymlu-explain.github+1​
10. Compared to a single decision tree, Random Forest tends to:
    a. Overfit more
    b. Overfit less
    c. Be less robust
    d. Produce less accurate models
    Answer: b. Overfit lessaiml+2​
11. In Random Forest, each decision tree is built using what portion of the data?
    a. The entire dataset
    b. Random samples with replacement
    c. Only labeled data
    d. A random third
    Answer: b. Random samples with replacementgithub+2​
12. Which ensemble technique is implemented by Random Forest?
    a. Boosting
    b. Stacking
    c. Bagging
    d. Clustering
    Answer: c. Baggingcsethings+2​
13. What metric can Random Forest provide for feature evaluation?
    a. Chi-square
    b. Gini importance
    c. Feature importance
    d. Odds ratio
    Answer: c. Feature importanceaiml+2​
14. Which of the following is an advantage of Random Forest?
    a. Handles missing data
    b. Requires strict feature scaling
    c. Only works on small datasets
    d. Can only run classification
    Answer: a. Handles missing datageeksforgeeks+1​
15. Random Forest handles outliers by:
    a. Ignoring them
    b. Removing them
    c. Being robust to outliers
    d. Normalizing all data
    Answer: c. Being robust to outlierscoursera+1​
16. Increasing the number of trees in a Random Forest results in:
    a. Less overfitting
    b. More overfitting
    c. No impact
    d. Decreased accuracy
    Answer: a. Less overfittingupgrad+2​
17. Random Forest is less interpretable compared to:
    a. Logistic Regression
    b. K-Means
    c. Single Decision Tree
    d. SVM
    Answer: c. Single Decision Treeupgrad+1​
18. Which of the following is NOT an advantage of Random Forest?
    a. High accuracy
    b. Models complex relationships
    c. Requires normalization
    d. Feature importance evaluation
    Answer: c. Requires normalizationupgrad+1​
19. The parameter that controls how many trees the forest has is commonly known as:
    a. n_estimators
    b. max_depth
    c. min_samples_split
    d. random_state
    Answer: a. n_estimatorsgeeksforgeeks+1​
20. What type of data can Random Forest handle?
    a. Only numerical
    b. Only categorical
    c. Both categorical and numerical
    d. Only binary
    Answer: c. Both categorical and numericalcareerfoundry+1​
21. Why does Random Forest use random features for each split?
    a. To reduce bias
    b. To create diversity among trees
    c. To increase variance
    d. To decrease data
    Answer: b. To create diversity among treeswikipedia+2​
22. Bagging in Random Forest refers to:
    a. Combining predictions using boosting
    b. Bootstrap aggregating
    c. Weighted average
    d. Sample splitting without replacement
    Answer: b. Bootstrap aggregatinggeeksforgeeks+1​
23. Which of the following methods can reduce overfitting in Random Forest?
    a. Increasing the number of trees
    b. Decreasing data size
    c. Using all features
    d. Reducing tree depth only
    Answer: a. Increasing the number of treesgithub+2​
24. Which of the following is true about Random Forest?
    a. Trees rely on each other
    b. Trees are independent
    c. All trees use the same data
    d. It is a lazy learner
    Answer: b. Trees are independentwikipedia+1​
25. What does the random part of Random Forest refer to?
    a. Random algorithmic output
    b. Random data and feature sampling
    c. Random model structure
    d. Random loss function
    Answer: b. Random data and feature samplingwikipedia+1​
26. What typically happens when the data used for each tree is not random in Random Forest?
    a. Performance improves
    b. Trees become similar
    c. Trees become more diverse
    d. Results are unchanged
    Answer: b. Trees become similarmlu-explain.github+1​
27. Which metric is commonly used to evaluate feature importance in Random Forest?
    a. Correlation coefficient
    b. Mean decrease impurity
    c. Sum of squares
    d. Precision
    Answer: b. Mean decrease impurityaiml+1​
28. Random Forest can be used to:
    a. Predict discrete classes
    b. Predict continuous values
    c. Both a and b
    d. Only visualize data
    Answer: c. Both a and bgeeksforgeeks+1​
29. Major advantage of Random Forest over a single tree is:
    a. Faster computation
    b. Lower bias
    c. Lower variance
    d. Easier implementation
    Answer: c. Lower variancegeeksforgeeks+1​
30. Default feature subset size at each split for classification in Random Forest is:
    a. All features
    b. Square root of total features
    c. Half of features
    d. One-third of features
    Answer: b. Square root of total featureswikipedia+1​
31. What happens if Random Forest trees are too deep?
    a. Overfitting increases
    b. Underfitting increases
    c. Bias increases
    d. Trees ignore data
    Answer: a. Overfitting increasesgithub+1​
32. What can Random Forest do with missing data?
    a. Cannot handle it
    b. Ignores only missing rows
    c. Handles missing data
    d. Replaces with mean only
    Answer: c. Handles missing dataaiml+1​
33. For imbalanced data, Random Forest is preferred because:
    a. Sensitive to class balance
    b. Prone to overfitting
    c. Less prone to overfitting on majority class
    d. Always exact
    Answer: c. Less prone to overfitting on majority classgithub+1​
34. Output of Random Forest classifier is:
    a. Single tree prediction
    b. Sum of predictions
    c. Highest vote among predictions
    d. None of the above
    Answer: c. Highest vote among predictionscareerfoundry+2​
35. Which of the following is a disadvantage of Random Forest?
    a. Less accurate
    b. Harder to interpret
    c. Requires normalized data
    d. Not flexible
    Answer: b. Harder to interpretcoursera+2​
36. In Random Forest, a tree is built until:
    a. It reaches a set depth
    b. It can’t be split anymore
    c. All data is used
    d. All features are used
    Answer: b. It can’t be split anymoregeeksforgeeks+1​
37. Random Forest does NOT require:
    a. Feature scaling
    b. Multiple decision trees
    c. Ensemble prediction
    d. Feature subset selection
    Answer: a. Feature scalingaiml+1​
38. Which data sampling technique is used by Random Forest?
    a. Sampling without replacement
    b. Sampling with replacement
    c. Simple random sample
    d. Systematic sampling
    Answer: b. Sampling with replacementgithub+2​
39. Random Forest bagging means:
    a. Each tree uses unique data
    b. Data for each tree is sampled with replacement
    c. Features are not sampled
    d. Trees depend on each other
    Answer: b. Data for each tree is sampled with replacementgithub+1​
40. Random Forest reduces:
    a. High bias
    b. High variance
    c. Both bias and variance
    d. Neither
    Answer: b. High variancegeeksforgeeks+1​
41. How does Random Forest help avoid overfitting?
    a. Combines predictions from diverse trees
    b. Only uses one set of features
    c. Uses only one tree
    d. Ignores variance
    Answer: a. Combines predictions from diverse treesgeeksforgeeks+1​
42. Random Forest estimates missing values by using:
    a. Only rows with data
    b. Nearest neighbor
    c. Other trees’ predictions
    d. Mean values only
    Answer: c. Other trees’ predictionsaiml+1​
43. Number of trees in Random Forest typically:
    a. Makes no difference
    b. Affects accuracy up to a point
    c. Decreases accuracy linearly
    d. Should be minimized
    Answer: b. Affects accuracy up to a pointupgrad+1​
44. What is the “forest” in Random Forest composed of?
    a. Linear models
    b. Regression models
    c. Decision trees
    d. SVMs
    Answer: c. Decision treescareerfoundry+1​
45. What aggregation method does Random Forest use for final output in classification?
    a. Weighted sum
    b. Median
    c. Majority voting
    d. Mode
    Answer: c. Majority votingcareerfoundry+2​
46. The principal problem randomization in Random Forest aims to solve is:
    a. Bias
    b. Overfitting
    c. Underfitting
    d. Under-sampling
    Answer: b. Overfittingupgrad+2​
47. If all trees in Random Forest are the same, the result is:
    a. No ensemble effect
    b. Increased diversity
    c. Higher accuracy
    d. None
    Answer: a. No ensemble effectmlu-explain.github+1​
48. The minimum size of features chosen at each split in classification Random Forest is:
    a. 1
    b. Log base 2 of features
    c. Square root of total features
    d. Total features
    Answer: c. Square root of total featureswikipedia+1​
49. For a dataset with 100 features, what is the typical feature subset size at each split (classification)?
    a. 10
    b. 100
    c. 50
    d. 20
    Answer: a. 10wikipedia+1​
50. Which statement is true about Random Forest predictions?
    a. Only uses a single best tree
    b. Each tree votes and the most common wins
    c. Only the deepest tree is used
    d. Trees are combined linearly
    Answer: b. Each tree votes and the most common winscareerfoundry+2​

1. https://www.geeksforgeeks.org/machine-learning/random-forest-algorithm-in-machine-learning/
2. https://www.ibm.com/think/topics/random-forest
3. https://en.wikipedia.org/wiki/Random_forest
4. https://www.geeksforgeeks.org/quizzes/random-forest-bagging-algorithm/
5. https://www.geeksforgeeks.org/machine-learning/what-are-the-advantages-and-disadvantages-of-random-forest/
6. https://www.upgrad.com/blog/introduction-random-forest-algorithm/
7. https://github.com/Devinterview-io/random-forest-interview-questions
8. https://careerfoundry.com/en/blog/data-analytics/what-is-random-forest/
9. https://mlu-explain.github.io/random-forest/
10. https://aiml.com/what-are-the-advantages-and-disadvantages-of-random-forest/
11. https://csethings.com/random-forest-mcqs/
12. https://www.gyansetu.in/blog/machine-learning-mcq-interview-preparation/
13. https://www.coursera.org/articles/advantages-and-disadvantages-of-random-forest
14. https://www.sanfoundry.com/machine-learning-questions-answers-random-forest-algorithm/
15. https://www.codechef.com/learn/course/machine-learning/CLASSIFYMOD/problems/MLPROB205
16. https://aimcqs.com/decision-tree
17. https://community.deeplearning.ai/t/quiz-question-refers-to-random-forest-but-answers-dont-include-the-main-idea/144039
18. https://ioaglobal.org/blog/random-forests-explained-simply-prediction-real-world-use/
19. https://www.youtube.com/watch?v=v6VJ2RO66Ag
20. https://www.sciencedirect.com/science/article/pii/S2665917424002216