MCQs

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Overall MCQs for Reference

1. What is the primary purpose of a decision tree in machine learning?
   a. Data clustering
   b. Decision control and prediction
   c. Data randomization
   d. Feature selection
   Answer: b. Decision control and predictioncoursera+1​
2. Which node in a decision tree represents the starting point for decision making?
   a. Branch node
   b. Leaf node
   c. Root node
   d. Internal node
   Answer: c. Root nodeseldon+1​
3. Decision trees are primarily used for which types of learning?
   a. Reinforcement learning
   b. Supervised learning
   c. Unsupervised learning
   d. Deep learning
   Answer: b. Supervised learningcoursera+1​
4. What is the main benefit of using decision trees for decision control?
   a. High variance
   b. Complexity
   c. Explainability
   d. Randomness
   Answer: c. Explainabilityherovired+1​
5. Which process helps prevent decision trees from becoming excessively complex?
   a. Splitting
   b. Combining
   c. Pruning
   d. Clustering
   Answer: c. Pruningsanfoundry+1​
6. In decision tree analysis, what are leaf nodes used to represent?
   a. Intermediate results
   b. Final decisions or outcomes
   c. Feature weights
   d. Model inputs
   Answer: b. Final decisions or outcomesseldon+1​
7. What common problem can occur if a decision tree is too deep?
   a. Overfitting
   b. Underfitting
   c. Bias
   d. Randomness
   Answer: a. Overfittinggeeksforgeeks+1​
8. Which term describes reducing the size of a decision tree to improve generalizability?
   a. Boosting
   b. Pruning
   c. Sampling
   d. Binning
   Answer: b. Pruningsanfoundry+1​
9. What is a decision boundary in a decision control context in machine learning?
   a. The limit where decisions change between classes
   b. The region of maximum randomness
   c. The root node location
   d. The deepest leaf node
   Answer: a. The limit where decisions change between classessimplilearn+1​
10. Which algorithm is commonly used for constructing decision trees?
    a. k-Means
    b. ID3
    c. PCA
    d. SVM
    Answer: b. ID3herovired+1​
11. What type of variable is predicted in classification decision trees?
    a. Numerical variable
    b. Time series variable
    c. Categorical variable
    d. Boolean variable
    Answer: c. Categorical variablecoursera+1​
12. In regression decision trees, what kind of variable is predicted?
    a. Categorical variable
    b. Continuous variable
    c. Random variable
    d. Class variable
    Answer: b. Continuous variableseldon+1​
13. What is a Markov decision process (MDP) used for in machine learning?
    a. Unsupervised clustering
    b. Reinforcement learning decision modeling
    c. Feature selection
    d. Ensemble modeling
    Answer: b. Reinforcement learning decision modelingyoutube​generativeaimasters​
14. What does the candidate-elimination algorithm control?
    a. Ensemble voting
    b. Set of candidate hypotheses
    c. Feature scaling
    d. Data randomization
    Answer: b. Set of candidate hypothesessanfoundry​
15. Which method increases robustness in decision control by combining multiple decision trees?
    a. Bagging
    b. Univariate splitting
    c. Gradient descent
    d. Feature scaling
    Answer: a. Baggingcoursera+1​
16. What is the main aim of boosting algorithms in decision control?
    a. Increase bias
    b. Reduce variance and bias
    c. Increase randomness
    d. Prune decision boundaries
    Answer: b. Reduce variance and biassanfoundry​
17. Which process describes assigning increased weights to misclassified samples during boosting?
    a. Clustering
    b. Weight decay
    c. Weight re-assignment
    d. Feature selection
    Answer: c. Weight re-assignmentsanfoundry​
18. Which node type in a decision tree splits the data based on feature tests?
    a. Leaf node
    b. Internal node
    c. Root node
    d. Output node
    Answer: b. Internal nodeseldon+1​
19. What machine learning concept involves generating rules for decision control from data patterns?
    a. Feature scaling
    b. Inductive learning
    c. Ensemble learning
    d. Hyperparameter tuning
    Answer: b. Inductive learningsanfoundry​
20. What problem is associated with shallow decision trees?
    a. Overfitting
    b. Underfitting
    c. High variance
    d. Random decisions
    Answer: b. Underfittinggeeksforgeeks+1​
21. Decision trees can be effectively visualized due to their:
    a. Simplicity
    b. Complexity
    c. Randomness
    d. High variance
    Answer: a. Simplicityherovired+1​
22. Which is a major drawback of deep decision trees?
    a. High explainability
    b. Overfitting risk
    c. Underfitting risk
    d. High bias
    Answer: b. Overfitting riskgeeksforgeeks+1​
23. Which type of decision tree is used for predicting continuous outcomes?
    a. Classification tree
    b. Regression tree
    c. Ensemble tree
    d. Random tree
    Answer: b. Regression treecoursera+1​
24. Which is true about the decision-making process in a random forest?
    a. Uses a single tree
    b. Aggregates outputs from multiple trees
    c. Relies on K-means clustering
    d. Uses root node only
    Answer: b. Aggregates outputs from multiple treesherovired+1​
25. Which method is effective for automating large-scale business decision-making processes?
    a. Decision control with ML
    b. Manual rule-writing
    c. Random assignment
    d. Heuristic evaluation
    Answer: a. Decision control with MLlatentview​
26. How does pruning affect decision control in a decision tree?
    a. Increases tree depth
    b. Reduces complexity and prevents overfitting
    c. Adds randomness
    d. Lowers model accuracy
    Answer: b. Reduces complexity and prevents overfittingseldon+1​
27. What is the main role of the cost function in decision tree construction?
    a. Determines split points
    b. Assigns weights to features
    c. Reduces overfitting
    d. Decides hyperparameters
    Answer: a. Determines split pointsseldon​
28. Which learning paradigm involves making decisions based on previous actions and outcomes?
    a. Supervised learning
    b. Reinforcement learning
    c. Unsupervised learning
    d. Semi-supervised learning
    Answer: b. Reinforcement learninggenerativeaimasters​youtube​
29. What is ensemble learning in the context of decision control?
    a. Using a single algorithm repeatedly
    b. Combining multiple models to improve decision accuracy
    c. Selecting random decisions
    d. Averaging input data
    Answer: b. Combining multiple models to improve decision accuracysanfoundry+1​
30. Which splitting criterion is commonly used in decision tree algorithms?
    a. Entropy
    b. Random selection
    c. Mean calculation
    d. Data binning
    Answer: a. Entropyherovired+1​
31. What does the term ‘decision control’ generally refer to in machine learning?
    a. Data visualization
    b. Systematic selection and prediction of outputs
    c. Random sampling
    d. Feature extraction
    Answer: b. Systematic selection and prediction of outputslatentview+1​
32. Which is a popular ensemble method that uses decision control for classification?
    a. PCA
    b. Random Forest
    c. K-means
    d. SVM
    Answer: b. Random Forestherovired+1​
33. How does feature importance affect decision control in a tree model?
    a. Determines order of random sampling
    b. Influences which splits are made in the tree
    c. Causes overfitting
    d. Reduces accuracy
    Answer: b. Influences which splits are made in the treeseldon​
34. Why is explainability important for decision control in machine learning?
    a. To make models opaque
    b. So results are interpretable by humans
    c. To add complexity
    d. To increase prediction errors
    Answer: b. So results are interpretable by humansherovired+1​
35. What type of decision problem is a yes/no classification in decision trees?
    a. Regression
    b. Supervised learning
    c. Reinforcement learning
    d. Unsupervised learning
    Answer: b. Supervised learningsimplilearn+1​
36. What does sequential decision-making refer to in reinforcement learning?
    a. Decisions are made in random order
    b. Each decision affects future decisions
    c. Only first decision matters
    d. Multiple decisions are made at once
    Answer: b. Each decision affects future decisionsyoutube​generativeaimasters​
37. What is the function of hyperparameters in decision control models?
    a. Control model training settings
    b. Select features randomly
    c. Decide output values directly
    d. Reduce variance
    Answer: a. Control model training settingsigmguru​
38. In decision trees, which method can help control the risk of overfitting?
    a. Shallow tree depth
    b. Deep tree depth
    c. Random split selection
    d. Increasing predictors
    Answer: a. Shallow tree depthgeeksforgeeks+1​
39. What does the ‘root node’ in a decision tree represent?
    a. The final decision outcome
    b. The beginning of decision-making process
    c. The low-importance feature
    d. The least splits
    Answer: b. The beginning of decision-making processcoursera+1​
40. Which is a key challenge for decision control in high-dimensional data?
    a. Reducing feature space
    b. Increasing node depth
    c. Limiting leaf nodes
    d. Adding randomness
    Answer: a. Reducing feature spaceseldon​
41. In a decision-tree-driven classification model, what should be minimized to improve generalization?
    a. Node randomness
    b. Tree depth
    c. Leaf node count
    d. Data input size
    Answer: b. Tree depthgeeksforgeeks+1​
42. Which technique is NOT commonly used for decision control in machine learning?
    a. Decision trees
    b. Random forests
    c. K-means clustering
    d. Boosting
    Answer: c. K-means clusteringyoutube​herovired​
43. What is the outcome of using cost-complexity pruning in decision trees?
    a. Increased accuracy on training data but less on test data
    b. Lower model complexity and improved generalization
    c. Random decision outcomes
    d. Increased depth
    Answer: b. Lower model complexity and improved generalizationsanfoundry+1​
44. Why is randomness introduced in some decision control algorithms?
    a. To improve model bias
    b. To reduce overfitting and improve generalization
    c. To maximize entropy
    d. To decrease data quality
    Answer: b. To reduce overfitting and improve generalizationsanfoundry+1​
45. Which decision control model is ideal for both regression and classification problems?
    a. PCA model
    b. Decision tree
    c. SVM
    d. K-means
    Answer: b. Decision treeherovired+1​
46. What is the role of the Gini impurity metric in decision tree control?
    a. Randomly select features
    b. Measure quality of splits
    c. Normalize data
    d. Assign class labels directly
    Answer: b. Measure quality of splitsseldon+1​
47. In automated decision-making using machine learning, what is typically required?
    a. Manual rule definition
    b. Model training and evaluation
    c. Randomized output
    d. Data encryption
    Answer: b. Model training and evaluationlatentview+1​
48. Which step is crucial for controlling decisions in ensemble models?
    a. Aggregating predictions from individual learners
    b. Assigning random values
    c. Ignoring feature importance
    d. Increasing tree depth
    Answer: a. Aggregating predictions from individual learnerssanfoundry+1​
49. Which is the best way to interpret the decisions taken by tree-based models?
    a. Analyze feature importance and visual tree structure
    b. Randomly sample leaf nodes
    c. Remove all decision branches
    d. Ignore root node
    Answer: a. Analyze feature importance and visual tree structureherovired+1​
50. Which is a well-known disadvantage of decision control using trees?
    a. Lack of interpretability
    b. Prone to overfitting in presence of many features
    c. Requires large data clustering
    d. Cannot be used for regression
    Answer: b. Prone to overfitting in presence of many featuresgeeksforgeeks+1​

1. https://www.coursera.org/articles/decision-tree-machine-learning
2. https://www.seldon.io/decision-trees-in-machine-learning/
3. https://herovired.com/learning-hub/blogs/top-machine-learning-question-answer/
4. https://www.sanfoundry.com/1000-machine-learning-questions-answers/
5. https://www.geeksforgeeks.org/machine-learning/machine-learning-interview-questions/
6. https://www.simplilearn.com/tutorials/machine-learning-tutorial/machine-learning-interview-questions
7. https://www.youtube.com/watch?v=FugpqOEPuBg
8. https://generativeaimasters.in/machine-learning-interview-questions-and-answers/
9. https://www.latentview.com/blog/the-role-of-machine-learning-in-automating-decision-making-processes/
10. https://www.igmguru.com/blog/machine-learning-mcqs
11. https://www.gyansetu.in/blog/machine-learning-mcq-interview-preparation/
12. https://www.geeksforgeeks.org/quizzes/machine-learning-quiz-questions-and-answers/
13. https://www.interviewbit.com/machine-learning-mcq/
14. https://interviewkickstart.com/blogs/articles/machine-learning-mcqs-answers
15. https://www.geeksforgeeks.org/artificial-intelligence/decision-making-in-ai/
16. https://www.scribd.com/document/754714735/T1-machine-learning-mcq-questions-and-answers-key
17. https://www.kaggle.com/questions-and-answers/534501
18. https://thedecisionlab.com/reference-guide/computer-science/machine-learning
19. https://unstop.com/blog/machine-learning-interview-questions
20. https://curatepartners.com/blogs/skills-tools-platforms/exploring-machine-learning-algorithms-the-future-of-ai-driven-decision-making-for-businesses/