🧪 Machine Learning (ML) MCQ Quiz Hub

1. Lasso can be interpreted as least-squares linear regression where
weights are regularized with the l1 norm
the weights have a gaussian prior
weights are regularized with the l2 norm
the solution algorithm is simpler

2. How can we best represent ‘support’ for the following association rule: “If X and Y, then Z”.
{x,y}/(total number of transactions)
{z}/(total number of transactions)
{z}/{x,y}
{x,y,z}/(total number of transactions)

3. Choose the correct statement with respect to ‘confidence’ metric in association rules
it is the conditional probability that a randomly selected transaction will include all the items in the consequent given that the transaction includes all the items in the antecedent.
a high value of confidence suggests a weak association rule
it is the probability that a randomly selected transaction will include all the items in the consequent as well as all the items in the antecedent.
confidence is not measured in terms of (estimated) conditional probability.

4. What are tree based classifiers?
classifiers which form a tree with each attribute at one level
classifiers which perform series of condition checking with one attributeat a time
both options except none
none of the options

5. What is gini index?
A. it is a type of index structure
it is a measure of purity
both options except none
none of the options

6. Which of the following sentences are correct in reference to Information gain? a. It is biased towards single-valued attributes b. It is biased towards multi-valued attributes c. ID3 makes use of information gain d. The approact used by ID3 is greedy
a and b
a and d
b, c and d
all of the above

7. his clustering approach initially assumes that each data instance represents a single cluster.
expectation maximization
k-means clustering
agglomerative clustering .
conceptual clustering

8. Which statement is true about the K-Means algorithm?
the output attribute must be cateogrical
all attribute values must be categorical
all attributes must be numeric
attribute values may be either categorical or numeric

9. KDD represents extraction of
data
knowledge
rules
model

10. The most general form of distance is
manhattan
eucledian
mean
minkowski

11. Which of the following algorithm comes under the classification
apriori
brute force
dbscan
k-nearest neighbor

12. Hierarchical agglomerative clustering is typically visualized as?
dendrogram
binary trees
block diagram
graph

13. The _______ step eliminates the extensions of (k-1)-itemsets which are not found to be frequent,from being considered for counting support
partitioning candidate generation
candidate generation
itemset eliminations
pruning

14. The distance between two points calculated using Pythagoras theorem is
supremum distance
eucledian distance
linear distance
manhattan distance

15. Which one of these is not a tree based learner?
cart
id3
bayesian classifier
random forest

16. Which one of these is a tree based learner?
rule based
bayesian belief network
bayesian classifier
random forest

17. What is the approach of basic algorithm for decision tree induct
greedy
top down
procedural
step by stepion?

18. Which of the following classifications would best suit the student performance classification systems?
if...then... analysis
market-basket analysis
regression analysis
cluster analysis

19. This clustering algorithm terminates when mean values computed for the current iteration of the algorithm are identical to the computed mean values for the previous iteration
k-means clustering
conceptual clustering
expectation maximization
agglomerative clustering

20. The number of iterations in apriori ___________ Select one:
increases with the size of the data
decreases with the increase in size of the data
increases with the size of the maximum frequent set
decreases with increase in size of the maximum frequent set

21. Frequent item sets is
superset of only closed frequent item sets
superset of only maximal frequent item sets
subset of maximal frequent item sets
superset of both closed frequent item sets and maximal frequent item sets

22. A good clustering method will produce high quality clusters with
high inter class similarity
low intra class similarity
high intra class similarity
no inter class similarity

23. Which Association Rule would you prefer
high support and medium confidence
high support and low confidence
low support and high confidence
low support and low confidence

24. In a Rule based classifier, If there is a rule for each combination of attribute values, what do you called that rule set R
exhaustive
inclusive
comprehensive
mutually exclusive

25. The apriori property means
if a set cannot pass a test, its supersets will also fail the same test
to decrease the efficiency, do level-wise generation of frequent item sets
to improve the efficiency, do level-wise generation of frequent item sets d.
if a set can pass a test, its supersets will fail the same test

26. If an item set ‘XYZ’ is a frequent item set, then all subsets of that frequent item set are
undefined
not frequent
frequent
can not say

27. Clustering is ___________ and is example of ____________learning
predictive and supervised
dpredictive and unsupervise
descriptive and supervised
descriptive and unsupervised

28. To determine association rules from frequent item sets
only minimum confidence needed
neither support not confidence needed
both minimum support and confidence are needed
minimum support is needed

29. If {A,B,C,D} is a frequent itemset, candidate rules which is not possible is
c –> a
d –>abcd
a –> bc
b –> adc

30. Which Association Rule would you prefer
high support and low confidence
low support and high confidence
low support and low confidence
high support and medium confidence

31. Classification rules are extracted from _____________
decision tree
root node
branches
siblings

32. What does K refers in the K-Means algorithm which is a non-hierarchical clustering approach?
complexity
fixed value
no of iterations
number of clusters

33. How will you counter over-fitting in decision tree?
by pruning the longer rules
by creating new rules
both by pruning the longer rules’ and ‘ by creating new rules’
none of the options

34. What are two steps of tree pruning work?
pessimistic pruning and optimistic pruning
postpruning and prepruning
cost complexity pruning and time complexity pruning
none of the options

35. Which of the following sentences are true?
in pre-pruning a tree is pruned by halting its construction early
a pruning set of class labelled tuples is used to estimate cost complexity
the best pruned tree is the one that minimizes the number of encodingbits
All of the above

36. Assume that you are given a data set and a neural network model trained on the data set. You are asked to build a decision tree model with the sole purpose of understanding/interpreting the built neural network model. In such a scenario, which among the following measures would you concentrate most on optimising?
accuracy of the decision tree model on the given data set
f1 measure of the decision tree model on the given data set
fidelity of the decision tree model, which is the fraction of instances on which the neuralnetwork and the decision tree give the same output
comprehensibility of the decision tree model, measured in terms of the size of the corresponding rule set

37. Which of the following properties are characteristic of decision trees? (a) High bias (b) High variance (c) Lack of smoothness of prediction surfaces (d) Unbounded parameter set
a and b
a and d
b, c and d
all of the above

38. To control the size of the tree, we need to control the number of regions. One approach to do this would be to split tree nodes only if the resultant decrease in the sum of squares error exceeds some threshold. For the described method, which among the following are true? (a) It would, in general, help restrict the size of the trees (b) It has the potential to affect the performance of the resultant regression/classification model (c) It is computationally infeasible
a and b
a and d
b, c and d
all of the above

39. Which among the following statements best describes our approach to learning decision trees
identify the best partition of the input space and response per partition to minimise sumof squares error
identify the best approximation of the above by the greedy approach (to identifying thepartitions
identify the model which gives the best performance using the greedy approximation(option (b)) with the smallest partition scheme
identify the model which gives performance close to the best greedy approximation performance (option (b)) with the smallest partition scheme

40. Having built a decision tree, we are using reduced error pruning to reduce the size of the tree. We select a node to collapse. For this particular node, on the left branch, there are 3 training data points with the following outputs: 5, 7, 9.6 and for the right branch, there are four training data points with the following outputs: 8.7, 9.8, 10.5, 11. What were the original responses for data points along the two branches (left & right respectively) and what is the new response after collapsing the node?
10.8, 13.33, 14.48
10.8, 13.33, 12.06
7.2, 10, 8.8
7.2, 10, 8.6

41. Suppose on performing reduced error pruning, we collapsed a node and observed an improvement in the prediction accuracy on the validation set. Which among the following statements are possible in light of the performance improvement observed? (a) The collapsed node helped overcome the effect of one or more noise affected data points in the training set (b) The validation set had one or more noise affected data points in the region corresponding to the collapsed node (c) The validation set did not have any data points along at least one of the collapsed branches (d) The validation set did have data points adversely affected by the collapsed node
a and b
a and d
b, c and d
all of the above

42. Time Complexity of k-means is given by
o(mn)
o(tkn)
o(kn)
o(t2kn)

43. In Apriori algorithm, if 1 item-sets are 100, then the number of candidate 2 item-sets are
100
200
4950
5000

44. Machine learning techniques differ from statistical techniques in that machine learning methods
are better able to deal with missing and noisy data
typically assume an underlying distribution for the data
have trouble with large-sized datasets
are not able to explain their behavior

45. The probability that a person owns a sports car given that they subscribe to automotive magazine is 40%. We also know that 3% of the adult population subscribes to automotive magazine. The probability of a person owning a sports car given that they don’t subscribe to automotive magazine is 30%. Use this information to compute the probability that a person subscribes to automotive magazine given that they own a sports car
0.0368
0.0396
0.0389
0.0398

46. What is the final resultant cluster size in Divisive algorithm, which is one of the hierarchical clustering approaches?
zero
three
singleton
two

47. Given a frequent itemset L, If |L| = k, then there are
2k – 1 candidate association rules
2k candidate association rules
2k – 2 candidate association rules
2k -2 candidate association rules

48. Which Statement is not true statement.
k-means clustering is a linear clustering algorithm.
k-means clustering aims to partition n observations into k clusters
k-nearest neighbor is same as k-means
k-means is sensitive to outlier

49. which of the following cases will K-Means clustering give poor results? 1. Data points with outliers 2. Data points with different densities 3. Data points with round shapes 4. Data points with non-convex shapes
1 and 2
2 and 3
2 and 4
1, 2 and 4

50. What is Decision Tree?
flow-chart
structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label
flow-chart like structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label
None of the above

51. What are two steps of tree pruning work?
pessimistic pruning and optimistic pruning
postpruning and prepruning
cost complexity pruning and time complexity pruning
none of the options

52. A database has 5 transactions. Of these, 4 transactions include milk and bread. Further, of the given 4 transactions, 2 transactions include cheese. Find the support percentage for the following association rule “if milk and bread are purchased, then cheese is also purchased”.
0.4
0.6
0.8
0.42

53. Which of the following option is true about k-NN algorithm?
I can be used for classification
??it can be used for regression
??it can be used in both classification and regression??
not useful in ml algorithm

54. How to select best hyperparameters in tree based models?
measure performance over training data
measure performance over validation data
both of these
random selection of hyper parameters

55. What is true about K-Mean Clustering? 1. K-means is extremely sensitive to cluster center initializations 2. Bad initialization can lead to Poor convergence speed 3. Bad initialization can lead to bad overall clustering
1 and 3
1 and 2
2 and 3
1, 2 and 3