Machine Learning Set 8 MCQs

Q1 | In Apriori algorithm, if 1 item-sets are 100, then the number of candidate 2 item-sets are

100

200

4950

5000

Q2 | Machine learning techniques differ from statistical techniques in that machine learning methods

are better able to deal with missing and noisy data

typically assume an underlying distribution for the data

have trouble with large-sized datasets

are not able to explain their behavior

Q3 | The probability that a person owns a sports car given that they subscribe to automotive magazine is 40%. We also know that 3% of the adult population subscribes to automotive magazine. The probability of a person owning a sports car given that they donâ€™t subscribe to automotive magazine is 30%. Use this information to compute the probability that a person subscribes to automotive magazine given that they own a sports car

0.0368

0.0396

0.0389

0.0398

Q4 | What is the final resultant cluster size in Divisive algorithm, which is one of the hierarchical clustering approaches?

zero

three

singleton

two

Q5 | Given a frequent itemset L, If |L| = k, then there are

2k – 1 candidate association rules

2k candidate association rules

2k – 2 candidate association rules

2k -2 candidate association rules

Q6 | Which Statement is not true statement.

k-means clustering is a linear clustering algorithm.

k-means clustering aims to partition n observations into k clusters

k-nearest neighbor is same as k-means

k-means is sensitive to outlier

Q7 | which of the following cases will K-Means clustering give poor results?1. Data points with outliers2. Data points with different densities3. Data points with round shapes4. Data points with non-convex shapes

1 and 2

2 and 3

2 and 4

1, 2 and 4

Q8 | What is Decision Tree?

flow-chart

structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label

flow-chart like structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label

none of the above

Q9 | 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

Q10 | A database has 5 transactions. Of these, 4 transactions include milk and bread. Further, of the given 4 transactions, 2 transactions include cheese. Find the support percentage for the following association rule “if milk and bread are purchased, then cheese is also purchased”.

0.4

0.6

0.8

0.42

Q11 | Which of the following option is true about k-NN algorithm?

it can be used for classification

??it can be used for regression

??it can be used in both classification and regression??

not useful in ml algorithm

Q12 | How to select best hyperparameters in tree based models?

measure performance over training data

measure performance over validation data

both of these

random selection of hyper parameters

Q13 | What is true about K-Mean Clustering?1. K-means is extremely sensitive to cluster center initializations2. Bad initialization can lead to Poor convergence speed3. Bad initialization can lead to bad overall clustering

1 and 3

1 and 2

2 and 3

1, 2 and 3

Q14 | 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 attribute at a time

both options except none

not possible

Q15 | What is gini index?

gini index??operates on the categorical target variables

it is a measure of purity

gini index performs only binary split

all (1,2 and 3)

Q16 | Tree/Rule based classification algorithms generate ... rule to perform the classification.

if-then.

while.

do while

switch.

Q17 | Decision Tree is

flow-chart

structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label

both a & b

class of instance

Q18 | Which of the following is true about Manhattan distance?

it can be used for continuous variables

it can be used for categorical variables

it can be used for categorical as well as continuous

it can be used for constants

Q19 | A company has build a kNN classifier that gets 100% accuracy on training data. When they deployed this model on client side it has been found that the model is not at all accurate. Which of the following thing might gone wrong?Note: Model has successfully deployed and no technical issues are found at client side except the model performance

it is probably a overfitted model

??it is probably a underfitted model

??can’t say

wrong client data

Q20 | hich of the following classifications would best suit the student performance classification systems?

if...then... analysis

market-basket analysis

regression analysis

cluster analysis

Q21 | Which statement is true about the K-Means algorithm? Select one:

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

Q22 | Which of the following can act as possible termination conditions in K-Means?1. For a fixed number of iterations.2. Assignment of observations to clusters does not change between iterations. Except for cases with a bad local minimum.3. Centroids do not change between successive iterations.4. Terminate when RSS falls below a threshold.

1, 3 and 4

1, 2 and 3

1, 2 and 4

1,2,3,4

Q23 | Which of the following statement is true about k-NN algorithm?1) k-NN performs much better if all of the data have the same scale2) k-NN works well with a small number of input variables (p), but struggles when the number of inputs is very large3) k-NN makes no assumptions about the functional form of the problem being solved

1 and 2

1 and 3

only 1

1,2 and 3

Q24 | In which of the following cases will K-means clustering fail to give good results? 1) Data points with outliers 2) Data points with different densities 3) Data points with nonconvex shapes

1 and 2

2 and 3

1, 2, and 3??

1 and 3

Q25 | 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’

over-fitting is not possible