Classification Algorithm-1 MCQs – Class 12 Data Science (844)
Practice high-quality, exam-oriented Classification Algorithm-1 MCQs to strengthen your concepts, crack the CBSE question pattern, and walk into exam fully prepared.
Q1. What is a Decision Tree?
a) A statistical regression formula
b) A diagrammatic representation of the decision-making process with a tree-like structure
c) A type of neural network
d) A clustering algorithm
Q2. In a decision tree, what does each internal node denote?
a) A leaf label
b) A branch outcome
c) A question on choosing a particular class
d) A random value
Q3. A student is designing a Decision Tree for deciding whether to carry an umbrella. The possible outcomes of the weather test are represented by branches. What do these branches represent?
a) Class labels
b) Outcomes of the test
c) Main objective
d) Mean of observations
Q4. What does each leaf node of a decision tree hold?
a) A test condition
b) A branch outcome
c) A class label
d) A probability distribution
Q5. Why are Decision Trees considered useful supervised learning methods?
a) They always require statistical knowledge
b) They are easy to visualize, understand and interpret
c) They can only handle numerical variables
d) They can only solve regression problems
Q6. When should Decision Trees be preferred over linear classification techniques?
a) When the trend in data is linear
b) When the trend in data is non-linear
c) When there is no data
d) When only two classes exist
Q7. Decision Trees can be applied to:
a) Only classification problems
b) Only regression problems
c) Both classification and regression problems
d) Neither classification nor regression
Q8. Which type of variables can Decision Trees handle?
a) Only numerical variables
b) Only categorical variables
c) Both numerical and categorical variables
d) Only binary variables
Q9. How much do outliers and missing values influence Decision Trees?
a) Heavily
b) Not much, to a fair degree
c) They completely destroy the model
d) Decision trees cannot handle any missing values
Q10. Regression trees are used when the dependent variable is:
a) Categorical
b) Continuous
c) Binary only
d) Text-based
Q11. Classification trees are used when the dependent variable is:
a) Continuous
b) Categorical
c) Numerical only
d) Random
Q12. In a regression tree, the value of the terminal node after training is the:
a) Mode of observations
b) Median of observations
c) Mean of observations
d) Maximum of observations
Q13. Lee Goldman’s decision tree was developed for use by:
a) Airline pilots
b) Submarine doctors
c) School teachers
d) Bank managers
Q14. What should be placed at the root of a decision tree?
a) A random test
b) The main objective/decision you are trying to make
c) The least important attribute
d) A leaf node
Q15. While creating a decision tree, if another decision has to be made, which node shape should be drawn?
a) Circular node
b) Triangular node
c) Square node
d) Oval node
Q16. If the outcome of a decision is not quite certain, which node shape should be drawn?
a) Square node
b) Circular node
c) Rectangular node
d) Diamond node
Q17. Assertion (A): Decision Trees can only be used for classification problems.
Reason (R): Decision Trees are versatile and can be used for both classification and regression problems.
a) Both A and R are true and R is the correct explanation of A
b) Both A and R are true but R is NOT the correct explanation of A
c) A is true but R is false
d) A is false but R is true
Q18. Assertion (A): In a regression tree, predictions on unseen data are made using the mode of the observations at the terminal node.
Reason (R): Regression trees are used when the dependent variable is continuous.
a) Both A and R are true and R is the correct explanation of A
b) Both A and R are true but R is NOT the correct explanation of A
c) A is true but R is false
d) A is false but R is true
Q19. Which of the following are parts of a Decision Tree?
a) Decision Node
b) Leaf Node
c) Branch
d) All of the above
Q20. Which of the following statement is false?
a) Decision Trees can contain only one branch.
b) Decision Trees can be used for classification and regression.
c) Random Forests algorithm uses Decision Trees.
d) None of the above
Q21. Which of the following is a use case for Decision Trees?
a) Classification
b) Regression
c) Both of the above
Q22. A decision tree can be further divided into further sub-trees.
a) True
b) False
Q23. Decision Trees are easier to understand and interpret.
a) True
b) False
