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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.
Q2 | Frequent item sets is
Q3 | A good clustering method will produce high quality clusters with
Q4 | Which statement is true about neural network and linear regression models?
Q5 | Which Association Rule would you prefer
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
Q7 | If an item set ‘XYZ’ is a frequent item set, then all subsets of that frequent item set are
Q8 | Clustering is ___________ and is example of ____________learning
Q9 | To determine association rules from frequent item sets
Q10 | If {A,B,C,D} is a frequent itemset, candidate rules which is not possible is
Q11 | Which Association Rule would you prefer
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
Q13 | Classification rules are extracted from _____________
Q14 | What does K refers in the K-Means algorithm which is a non-hierarchical clustering approach?
Q15 | How will you counter over-fitting in decision tree?
Q16 | What are two steps of tree pruning work?
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
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
Q19 | Which among the following statements best describes our approach to learning decision trees
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?
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
Q22 | Time Complexity of k-means is given by