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This set of Machine Learning (ML) Multiple Choice Questions & Answers (MCQs) focuses on Machine Learning Set 6
Q1 | 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)
Q2 | 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.
Q3 | 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
Q4 | Which of the following sentences are correct in reference toInformation gain?a. It is biased towards single-valued attributesb. It is biased towards multi-valued attributesc. ID3 makes use of information gaind. The approact used by ID3 is greedy
- a and b
- a and d
- b, c and d
- all of the above
Q5 | Multivariate split is where the partitioning of tuples is based on acombination of attributes rather than on a single attribute.
- true
- false
Q6 | Gain ratio tends to prefer unbalanced splits in which one partition is much smaller than the other
- true
- false
Q7 | The gini index is not biased towards multivalued attributed.
- true
- false
Q8 | Gini index does not favour equal sized partitions.
- true
- false
Q9 | When the number of classes is large Gini index is not a good choice.
- true
- false
Q10 | Attribute selection measures are also known as splitting rules.
- true
- false
Q11 | his clustering approach initially assumes that each data instance represents a single cluster.
- expectation maximization
- k-means clustering
- agglomerative clustering
- conceptual clustering
Q12 | 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
Q13 | KDD represents extraction of
- data
- knowledge
- rules
- model
Q14 | The most general form of distance is
- manhattan
- eucledian
- mean
- minkowski
Q15 | Which of the following algorithm comes under the classification
- apriori
- brute force
- dbscan
- k-nearest neighbor
Q16 | Hierarchical agglomerative clustering is typically visualized as?
- dendrogram
- binary trees
- block diagram
- graph
Q17 | 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
- itemset eliminations
- pruning
Q18 | The distance between two points calculated using Pythagoras theorem is
- supremum distance
- eucledian distance
- linear distance
- manhattan distance
Q19 | Which one of these is not a tree based learner?
- cart
- id3
- bayesian classifier
- random forest
Q20 | Which one of these is a tree based learner?
- rule based
- bayesian belief network
- bayesian classifier
- random forest
Q21 | Which of the following classifications would best suit the student performance classification systems?
- if...then... analysis
- market-basket analysis
- regression analysis
- cluster analysis
Q22 | 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