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 7 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

Q61.

Which of the following is correct about regularized regression?

Can help with bias trade-off

Cannot help with model selection

Cannot help with variance trade-off

All of the mentioned

Discuss

Answer: (a).Can help with bias trade-off

Q62.

Point out the wrong statement.

Model based approach may be computationally convenient

Model based approach use Bayes theorem

Model based approach are reasonably inaccurate on real problems

All of the mentioned

Discuss

Answer: (c).Model based approach are reasonably inaccurate on real problems

Q63.

Which of the following methods are present in caret for regularized regression?

ridge

lasso

relaxo

all of the mentioned

Discuss

Answer: (d).all of the mentioned

Q64.

Which of the following method can be used to combine different classifiers?

Model stacking

Model combining

Model structuring

None of the mentioned

Discuss

Answer: (a).Model stacking

Q65.

Point out the correct statement.

Combining classifiers improves interpretability

Combining classifiers reduces accuracy

Combining classifiers improves accuracy

All of the mentioned

Discuss

Answer: (c).Combining classifiers improves accuracy

Q66.

Which of the following function provides unsupervised prediction?

cl_forecast

cl_nowcast

cl_precast

none of the mentioned

Discuss

Answer: (d).none of the mentioned

Q67.

Model based prediction considers relatively easy version for covariance matrix.

True

False

May be True or False

Can't say

Discuss

Answer: (b).False

Q68.

Which of the following is used to assist the quantitative trader in the development?

quantmod

quantile

quantity

mboost

Discuss

Answer: (a).quantmod

Q69.

Which of the following function can be used for forecasting?

predict

forecast

ets

all of the mentioned

Discuss

Answer: (b).forecast

Q70.

Predictive analytics is same as forecasting.

True

False

May be True or False

Can't say

Discuss

Answer: (b).False

Page 7 of 7

Welcome to the Machine Learning MCQs Page

Machine Learning MCQs | Page 7 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

Suggested Topics

Systems Programming

UNIX

HTML

C# programming

Embedded Systems

Data Structures and Algorithms

Object Oriented Programming

Data Warehousing and OLAP

SQL Server