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XAI - Assignment II

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Computers

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XAI - Assignment II
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20 questions

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1.

MULTIPLE CHOICE QUESTION

45 sec • 1 pt

Which of the following statements is not true about the Decision tree?

It can be applied on binary classification problems only

It is a predictor that predicts the label associated with an instance by traveling from a root node of a tree to a leaf

At each node, the successor child is chosen on the basis of a splitting of the input space

The splitting is based on one of the features or on a predefined set of splitting rules

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Media Image

Consider the figure. If person A starts driving at 8:30 AM and there are no other vehicles on the road, and another person B starts driving at 10 AM and there is an accident on the road, what will be the commute time of A and B respectively?

LONG, LONG

LONG, SHORT

SHORT, LONG

SHORT, SHORT

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Media Image

What does the following figure represent?

Decision tree for OR

Decision tree for AND

Decision tree for XOR

Decision tree for XNOR

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the main advantage of using the Random Forest algorithm?


It always gives 100% accuracy

It reduces overfitting and improves generalization

It requires only one decision tree for better performance

It works only for classification problems

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

How does Random Forest prevent overfitting?

By using a single deep decision tree

By selecting all features at each split

By averaging predictions from multiple trees trained on different subsets of data

By using only the most important features

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

How does Random Forest select features at each split in a tree?

It considers all features every time

It uses only the most correlated features

It removes features with missing values

It selects a random subset of features

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is a key characteristic of Random Forest?


It requires extensive data preprocessing

It works only with numerical data

It is a supervised learning algorithm


It cannot handle missing values

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