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MCQ

This document contains 11 multiple choice questions that test knowledge of machine learning concepts like decision trees, logistic regression, and clustering. Decision trees can be used for both classification and regression tasks. A leaf node is the end of a decision tree that cannot be split further. Logistic regression predicts the probability of a categorical dependent variable. Clustering belongs to unsupervised data analysis where the variable being predicted in regression is the response or dependent variable.

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Roshna Ravindran
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225 views2 pages

MCQ

This document contains 11 multiple choice questions that test knowledge of machine learning concepts like decision trees, logistic regression, and clustering. Decision trees can be used for both classification and regression tasks. A leaf node is the end of a decision tree that cannot be split further. Logistic regression predicts the probability of a categorical dependent variable. Clustering belongs to unsupervised data analysis where the variable being predicted in regression is the response or dependent variable.

Uploaded by

Roshna Ravindran
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as DOCX, PDF, TXT or read online on Scribd
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1. Decision tree can be used for ______.

(A) classification
(B) regression
(C) Both
(D) None of these

2. What is a leaf or terminal node in the decision tree?

(A)The end of the decision tree where it cannot be split


into further sub-nodes.
(B) Maximum depth
(C) A subsection of the entire tree
(D) A node that represents the entire population or
sample

3. In Decision Trees, for predicting a class label, the


algorithm starts from which node of the tree?
(A) Root
(B) Leaf
(C) Terminal
(D) Sub-node
4. What is splitting in the decision tree?
(A) Dividing a node into two or more sub-nodes based on if-
else conditions
(B) Removing a sub-node from the tree
(C) Balance the dataset prior to fitting
(D) All of the above
5. Logistic Regression is a Machine Learning algorithm
that is used to predict the probability of a ___?
(A) categorical independent variable
(B) categorical dependent variable.
(C) numerical dependent variable.
(D) numerical independent variable.
6. ___________ is a measure of uncertainty of a random variable.
 a) Information gain.
 b) Entropy.
 c) Gini Index.
 d) None of the above.

7. Data Analysis is a process of,

A. Inspecting data
B. Data Cleaning
C. Transforming of data
D. All of the mentioned above

8. Logistic regression is used to find the probability of event = Success and


event = ____.

A. Failure
B. Success
C. Both A and B
D. None of the mentioned above

9. Clustering belongs to ___ data analysis.

A. Supervised
B. Unsupervised
C. Both A and B
D. None of the mentioned above

10. In regression analysis, the variable that is being predicted is the


a. response, or dependent, variable
b. independent variable
c. intervening variable
d. is usually x

11. 0 and 1, or pass and fail or true and false is an example of?
A. Multinomial Logistic Regression
B. Binary Logistic Regression
C. Ordinal Logistic Regression
D. None of the above

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