1. Classification is a type of ______ learning technique.

A . Unsupervised

B . Reinforcement

C . Supervised

D . Semi-supervised

Answer

2. In the classification process, the model is built during the ______ phase.

A . Testing

B . Learning

C . Validation

D . Execution

Answer

3. The main goal of a classifier is to predict ______ for new data.

A . Patterns

B . Clusters

C . Class labels

D . Probabilities

Answer

4. A Decision Tree is a ______ structure.

A . Linear

B . Graph-like

C . Tree-like

D . Circular

Answer

5. The topmost node in a decision tree is called the ______ node.

A . Parent

B . Root

C . Child

D . Leaf

Answer

6. The attribute with the highest information gain is selected as the ______.

A . Target attribute

B . Root node

C . Leaf node

D . Noise attribute

Answer

7. The measure of impurity or uncertainty in data is called ______.

A . Information gain

B . Entropy

C . Variance

D . Precision

Answer

8. Information Gain is defined as the difference between original entropy and ______ entropy.

A . Expected

B . Split

C . Conditional

D . Attribute

Answer

9. ID3 algorithm was developed by ______.

A . Thomas Bayes

B . J. Ross Quinlan

C . Arthur Samuel

D . Geoffrey Hinton

Answer

10. The Bayesian classifier is based on ______ theorem.

A . Bayes`

B . Markov`s

C . Newton`s

D . Laplace`s

Answer

11. The Naïve Bayes classifier assumes ______ among predictors.

A . Dependence

B . Independence

C . Correlation

D . Redundancy

Answer

12. In Bayes` theorem, ( P(H|X) ) represents the ______ probability.

A . Prior

B . Posterior

C . Joint

D . Marginal

Answer

13. Rule-based classification uses a set of ______ rules for classification.

A . IF–THEN

B . FOR–WHILE

C . WHEN–ELSE

D . IF–ELSEIF

Answer

14. Coverage of a rule measures the ______ of tuples covered by the rule.

A . Number

B . Percentage

C . Weight

D . Depth

Answer

15. Accuracy of a rule measures the percentage of tuples that are ______ classified.

A . Randomly

B . Incorrectly

C . Correctly

D . Partially

Answer

16. Eager learners build a model ______ receiving new data tuples.

A . Before

B . After

C . During

D . Without

Answer

17. The most popular lazy learner is the ______ classifier.

A . Decision Tree

B . Naïve Bayes

C . K-Nearest Neighbor

D . Neural Network

Answer

18. The commonly used value of K in K-NN algorithm is ______.

A . 2

B . 3

C . 4

D . 5

Answer

19. Euclidean distance measures the ______ between two points.

A . Straight-line distance

B . Manhattan distance

C . Angular difference

D . Cosine similarity

Answer

20. The Manhattan distance is also called the ______ distance.

A . Air

B . Taxicab

C . Hamming

D . Cartesian

Answer

Fill in the Blanks

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21. Classification is used to categorize data into predefined ______.

Answer

22. Decision trees can be used for both ______ and numerical data.

Answer

23. Data classification is a two-step process, consisting of a __________________ and ___________________.

Answer

24. Entropy is a measure of ______ in the data.

Answer

25. _____________________ is a statistical technique used to classify the data based on probabilistic reasoning.

Answer

26. Information gain is used to select the best attribute for ______.

Answer

27. The formula for Bayes’ theorem is _____________________.

Answer

28. The "IF" part of a rule is known as the 

Answer

29. The "THEN" part of a rule is known as the 

Answer

30. Lazy learners perform generalization only when a ______ tuple is encountered.

Answer

31. In K-NN, classification is done based on the majority class among the ______ neighbors.

Answer

32. _____________distance measure is most commonly used in K-NN.

Answer

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