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Welcome to the Machine Learning MCQs Page

Dive deep into the fascinating world of Machine Learning with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Machine Learning, a crucial aspect of Data Science. In this section, you will encounter a diverse range of MCQs that cover various aspects of Machine Learning, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within Data Science.

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Check out the MCQs below to embark on an enriching journey through Machine Learning. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Data Science.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Machine Learning. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Machine Learning MCQs | Page 2 of 7

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Q11.
Which of the following function is a wrapper for different lattice plots to visualize the data?

a.

levelplot

b.

featurePlot

c.

plotsample

d.

none of the mentioned

Discuss
Answer: (b).featurePlot
Q12.
Point out the wrong statement.

a.

In every situation, the data generating mechanism can create predictors that only have a single unique value

b.

Predictors might have only a handful of unique values that occur with very low frequencies

c.

The function findLinearCombos uses the QR decomposition of a matrix to enumerate sets of linear combinations

d.

All of the mentioned

Discuss
Answer: (a).In every situation, the data generating mechanism can create predictors that only have a single unique value
Q13.
Which of the following function can be used to identify near zero-variance variables?

a.

zeroVar

b.

nearVar

c.

nearZeroVar

d.

all of the mentioned

Discuss
Answer: (c).nearZeroVar
Q14.
Which of the following function can be used to flag predictors for removal?

a.

searchCorrelation

b.

findCausation

c.

findCorrelation

d.

none of the mentioned

Discuss
Answer: (c).findCorrelation
Q15.
Point out the correct statement.

a.

findLinearColumns will also return a vector of column positions can be removed to eliminate the linear dependencies

b.

findLinearCombos will return a list that enumerates dependencies

c.

the function findLinearRows can be used to generate a complete set of row variables from one factor

d.

none of the mentioned

Discuss
Answer: (b).findLinearCombos will return a list that enumerates dependencies
Q16.
Which of the following can be used to impute data sets based only on information in the training set?

a.

postProcess

b.

preProcess

c.

process

d.

all of the mentioned

Discuss
Answer: (b).preProcess
Q17.
The function preProcess estimates the required parameters for each operation.

a.

True

b.

False

c.

May be True or False

d.

Can't say

Discuss
Answer: (a).True
Q18.
Which of the following can also be used to find new variables that are linear combinations of the original set with independent components?

a.

ICA

b.

SCA

c.

PCA

d.

None of the mentioned

Discuss
Answer: (a).ICA
Q19.
Which of the following function is used to generate the class distances?

a.

preprocess.classDist

b.

predict.classDist

c.

predict.classDistance

d.

all of the mentioned

Discuss
Answer: (b).predict.classDist
Q20.
The preProcess class can be used for many operations on predictors.

a.

True

b.

False

c.

May be True or False

d.

Can't say

Discuss
Answer: (a).True
Page 2 of 7

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