Data Science and Machine Learning (Theory and Projects) A to Z - Multiple Random Variables: Homework

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Information Technology (IT), Architecture, Social Studies
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5 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the main task of the homework assignment?
Implement a k-nearest neighbors classifier
Implement a support vector machine
Implement a Naive Bayes classifier
Implement a decision tree classifier
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which dataset is used for the Naive Bayes classifier task?
CIFAR-10 dataset
Iris dataset
MNIST dataset
Titanic dataset
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the key assumption made in a Naive Bayes classifier?
All features are equally important
All features are irrelevant
All features are independent given the class
All features are dependent on each other
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What should be done after building the Naive Bayes classifier?
Use only one feature for classification
Split the data into training and test sets
Use the entire dataset for training
Ignore the test data
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why might using all attributes independently not be a valid assumption?
It simplifies the model
It is the only valid assumption
It may deteriorate the result
It always improves the result
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