Top 350+ Solved Machine Learning (ML) MCQ Questions Answer
Q. Clustering is ___________ and is example of ____________learning
a. predictive and supervised
b. predictive and unsupervised
c. descriptive and supervised
d. descriptive and unsupervised
Q. To determine association rules from frequent item sets
a. only minimum confidence needed
b. neither support not confidence needed
c. both minimum support and confidence are needed
d. minimum support is needed
Q. If {A,B,C,D} is a frequent itemset, candidate rules which is not possible is
a. c –> a
b. d –>abcd
c. a –> bc
d. b –> adc
Q. Which Association Rule would you prefer
a. high support and low confidence
b. low support and high confidence
c. low support and low confidence
d. high support and medium confidence
Q. 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
a. conceptual clustering
b. k-means clustering
c. expectation maximization
d. agglomerative clustering
Q. Classification rules are extracted from _____________
a. decision tree
b. root node
c. branches
d. siblings
Q. What does K refers in the K-Means algorithm which is a non-hierarchical clustering approach?
a. complexity
b. fixed value
c. no of iterations
d. number of clusters
Q. How will you counter over-fitting in decision tree?
a. by pruning the longer rules
b. by creating new rules
c. both by pruning the longer rules’ and ‘ by creating new rules’
d. none of the options
Q. What are two steps of tree pruning work?
a. pessimistic pruning and optimistic pruning
b. postpruning and prepruning
c. cost complexity pruning and time complexity pruning
d. none of the options
Q. Which of the following sentences are true?
a. in pre-pruning a tree is \pruned\ by halting its construction early
b. a pruning set of class labelled tuples is used to estimate cost complexity
c. the best pruned tree is the one that minimizes the number of encodingbits
d. all of the above
Q. Assume that you are given a data set and a neural network model trained on the data set. Youare asked to build a decision tree model with the sole purpose of understanding/interpretingthe built neural network model. In such a scenario, which among the following measures wouldyou concentrate most on optimising?
a. accuracy of the decision tree model on the given data set
b. f1 measure of the decision tree model on the given data set
c. fidelity of the decision tree model, which is the fraction of instances on which the neuralnetwork and the decision tree give the same output
d. comprehensibility of the decision tree model, measured in terms of the size of the corresponding rule set
Q. 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. a and b
b. a and d
c. b, c and d
d. all of the above
Q. To control the size of the tree, we need to control the number of regions. One approach todo this would be to split tree nodes only if the resultant decrease in the sum of squares errorexceeds 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/classificationmodel(c) It is computationally infeasible
a. a and b
b. a and d
c. b, c and d
d. all of the above
Q. Which among the following statements best describes our approach to learning decision trees
a. identify the best partition of the input space and response per partition to minimise sumof squares error
b. identify the best approximation of the above by the greedy approach (to identifying thepartitions)
c. identify the model which gives the best performance using the greedy approximation(option (b)) with the smallest partition scheme
d. identify the model which gives performance close to the best greedy approximation performance (option (b)) with the smallest partition scheme
Q. Having built a decision tree, we are using reduced error pruning to reduce the size of thetree. We select a node to collapse. For this particular node, on the left branch, there are 3training data points with the following outputs: 5, 7, 9.6 and for the right branch, there arefour training data points with the following outputs: 8.7, 9.8, 10.5, 11. What were the originalresponses for data points along the two branches (left & right respectively) and what is thenew response after collapsing the node?
a. 10.8, 13.33, 14.48
b. 10.8, 13.33, 12.06
c. 7.2, 10, 8.8
d. 7.2, 10, 8.6