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This set of Machine Learning (ML) Multiple Choice Questions & Answers (MCQs) focuses on Machine Learning Set 7

Q1 | The number of iterations in apriori ___________ Select one: a. b. c. d.
  • 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
Q2 | 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
Q3 | 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
Q4 | Which statement is true about neural network and linear regression models?
  • both techniques build models whose output is determined by a linear sum of weighted input attribute values
  • the output of both models is a categorical attribute value
  • both models require numeric attributes to range between 0 and 1
  • both models require input attributes to be numeric
Q5 | 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
Q6 | 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
Q7 | 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
Q8 | Clustering is ___________ and is example of ____________learning
  • predictive and supervised
  • predictive and unsupervised
  • descriptive and supervised
  • descriptive and unsupervised
Q9 | 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
Q10 | If {A,B,C,D} is a frequent itemset, candidate rules which is not possible is
  • c –> a
  • d –>abcd
  • a –> bc
  • b –> adc
Q11 | 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
Q12 | 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
  • conceptual clustering
  • k-means clustering
  • expectation maximization
  • agglomerative clustering
Q13 | Classification rules are extracted from _____________
  • decision tree
  • root node
  • branches
  • siblings
Q14 | 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
Q15 | 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
Q16 | 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
Q17 | 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
Q18 | 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 and b
  • a and d
  • b, c and d
  • all of the above
Q19 | 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
Q20 | 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?
  • 10.8, 13.33, 14.48
  • 10.8, 13.33, 12.06
  • 7.2, 10, 8.8
  • 7.2, 10, 8.6
Q21 | 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 statementsare 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
Q22 | Time Complexity of k-means is given by
  • o(mn)
  • o(tkn)
  • o(kn)
  • o(t2kn)