Machine Learning Set 6 MCQs

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