Practical Data Science using Python - Naive Bayes Probability Model - Introduction
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Information Technology (IT), Architecture
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University
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Practice Problem
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Hard
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10 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is a key characteristic of the Naive Bayes classifier?
It is simple and fast.
It is only used for regression problems.
It requires extensive parameter tuning.
It is highly complex and slow.
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which of the following is NOT a typical application of Naive Bayes?
Disease detection
Image recognition
Sentiment analysis
Spam filtering
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What does the Bayes theorem help calculate in Naive Bayes classification?
The prior probability of features
The posterior probability of features
The probability of a class given features
The likelihood of features
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
In the context of Naive Bayes, what is a 'label'?
A constant value
A predictor variable
A target variable
A feature variable
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What assumption does the Naive Bayes classifier make about features?
Features are dependent on each other.
Features are independent of each other.
Features are correlated with the target variable.
Features are irrelevant to the classification.
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
In the employee attrition example, which feature is NOT considered a predictor?
Latest rating
Salary drawn
Employee ID
Years of experience
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What type of data does the Gaussian Naive Bayes classifier work with?
Categorical data
Gaussian distributed data
Binary data
Time series data
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