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.

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 6 of 7

Explore more Topics under Data Science

01

Data Science Basics

02

Data Analysis with Python

03

Data Manipulation

04

Data Analysis and Research

05

Statistical Inference and Regression Models

06

Developing Data Products

Q51.

Predicting with trees evaluate _____________ within each group of data.

equality

homogeneity

heterogeneity

all of the mentioned

Discuss

Answer: (b).homogeneity

Q52.

Point out the wrong statement.

Training and testing data must be processed in different way

Test transformation would mostly be imperfect

The first goal is statistical and second is data compression in PCA

All of the mentioned

Discuss

Answer: (a).Training and testing data must be processed in different way

Q53.

Which of the following method options is provided by train function for bagging?

bagEarth

treebag

bagFDA

all of the mentioned

Discuss

Answer: (d).all of the mentioned

Q54.

Which of the following is correct with respect to random forest?

Random forest are difficult to interpret but often very accurate

Random forest are easy to interpret but often very accurate

Random forest are difficult to interpret but very less accurate

None of the mentioned

Discuss

Answer: (a).Random forest are difficult to interpret but often very accurate

Q55.

Point out the correct statement.

Prediction with regression is easy to implement

Prediction with regression is easy to interpret

Prediction with regression performs well when linear model is correct

All of the mentioned

Discuss

Answer: (d).All of the mentioned

Q56.

Which of the following library is used for boosting generalized additive models?

gamBoost

gbm

ada

all of the mentioned

Discuss

Answer: (a).gamBoost

Q57.

The principal components are equal to left singular values if you first scale the variables.

True

False

May be True or False

Can't say

Discuss

Answer: (b).False

Q58.

Which of the following is statistical boosting based on additive logistic regression?

gamBoost

gbm

ada

mboost

Discuss

Answer: (a).gamBoost

Q59.

Which of the following is one of the largest boost subclass in boosting?

variance boosting

gradient boosting

mean boosting

all of the mentioned

Discuss

Answer: (b).gradient boosting

Q60.

PCA is most useful for non linear type models.

True

False

May be True or False

Can't say

Discuss

Answer: (b).False

Page 6 of 7

Welcome to the Machine Learning MCQs Page

Machine Learning MCQs | Page 6 of 7

Explore more Topics under Data Science

Data Science Basics

Data Analysis with Python

Data Manipulation

Data Analysis and Research

Statistical Inference and Regression Models

Developing Data Products

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