Decision Tree Machine Learning Multiple Choice Questions and answers pdf

Hello friends in this post we will discuss about Decision Tree Machine Learning Multiple Choice Questions and there answers.

Decision Tree is important topic in the Machine Learning chapter. These Notes are taken from Geeksforgeeks 

1. Decision tree is the most powerful for ________

A) classification
B) prediction
C) both a and b
D) none of these

ANSWER= C) both a and b
Explain:-Decision tree is the most powerful for classification and prediction
Check Answer

2. Decision tree is a flowchart like_________

A) leaf structure
B) tree structure
C) steam
D) none of these

ANSWER= B) tree structure
Explain:-Decision tree is a flowchart like tree structure
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3. Decision trees can handle_________

A) High dimensional data
B) low diamesional data
C) medium dimensional data
D) none of these

ANSWER= A) High dimensional data
Explain:- Decision trees can handle High dimensional data
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4.Which of the following is not strength of Decision Tree ?

A) able to generate understandable rules
B) able to handle both continuous and categorical variables
C) less appropriate for estimation tasks
D) perform classification without requiring much computation

ANSWER= C) less appropriate for estimation tasks
Explain:-
Check Answer

5.Which of the following is not weakness of Decision Tree ?

A) able to generate understandable rules
B) can be computationally expensive to train
C) less appropriate for estimation tasks
D) prone to errors in classification problems with many class

ANSWER= A) able to generate understandable rule
Explain:-
Check Answer

6.Decision-tree algorithm falls under the category of ________

A)unsupervised learning algorithms
B) reinforcement learning algorithm
C) supervised learning algorithms
D) prone to errors in classification problems with many class

ANSWER= C) supervised learning algorithms
Explain:- Decision-tree algorithm falls under the category of supervised learning algorithms.
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7.In Decision-tree algorithm At the beginning, we consider the whole training set as ________

A) leaf
B) root
C) steam
D) none of these

ANSWER= B) root
Explain:- At the beginning, we consider the whole training set as root.
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8._______is the measure of uncertainty of a random variable, it characterizes the impurity of an arbitrary collection of examples.

A) Information Gain
B) Gini Index
C) Entropy
D) none of these

ANSWER= C) Entropy
Explain:- Entropy is the measure of uncertainty of a random variable, it characterizes the impurity of an arbitrary collection of examples.
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9._______is a metric to measure how often a randomly chosen element would be incorrectly identified.

A) Information Gain
B) Gini Index
C) Entropy
D) none of these

ANSWER=B) Gini Index
Explain:- Gini Index is a metric to measure how often a randomly chosen element would be incorrectly identified
Check Answer