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Naive Bayes Classification Concepts

Authored by Ekta Gandotra

Engineering

University

Naive Bayes Classification Concepts
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10 questions

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1.

MULTIPLE CHOICE QUESTION

10 sec • 1 pt

What type of machine learning algorithm is Naive Bayes Classification?

Supervised learning

Unsupervised learning

Reinforcement learning

Semi-supervised learning

2.

MULTIPLE CHOICE QUESTION

10 sec • 1 pt

What is the main goal of classification in machine learning?

To group similar data points together without prior labels.

To predict a continuous output value based on input features.

To assign data points to predefined categories or classes.

To discover hidden patterns in unlabeled data.

3.

MULTIPLE CHOICE QUESTION

10 sec • 1 pt

On which mathematical principle is Naive Bayes Classification primarily based?

Linear Regression

Decision Trees

Bayes' Theorem

Support Vector Machines

4.

MULTIPLE CHOICE QUESTION

10 sec • 1 pt

In Naive Bayes Classification, what does the term "Naive" signify regarding the variables used?

The variables are complex and difficult to understand.

The variables are assumed to be dependent on each other.

The variables are assumed to be independent of each other.

The algorithm is simple and does not require many variables.

5.

MULTIPLE CHOICE QUESTION

10 sec • 1 pt

As explained in the video, what type of probability does Bayes' Theorem primarily deal with?

Joint probability

Marginal probability

Conditional probability (specifically "reverse" or "cause" probability)

Unconditional probability

6.

MULTIPLE CHOICE QUESTION

10 sec • 1 pt

In Naive Bayes classification, why can the denominator P(X) often be disregarded when comparing probabilities for different classes?

It is always equal to 1.

It is a constant across all class calculations.

It represents the prior probability of the features.

It is only used for normalization, not classification.

7.

MULTIPLE CHOICE QUESTION

10 sec • 1 pt

What is the fundamental assumption of the Naive Bayes classifier?

Features are dependent on each other.

Features are conditionally independent given the class.

All features have equal importance.

The prior probability of all classes is equal.

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