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

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

Assessment

Interactive Video

Information Technology (IT), Architecture, Social Studies

University

Hard

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The video tutorial guides students through implementing a naive Bayes classifier in Python using the iris dataset from seaborn. It emphasizes the assumption of independent random variables given the class category and instructs on modeling distributions and building a joint distribution. Students are reminded to split data into training and test sets and to report classification results. The tutorial concludes with a discussion on the potential impact of assumptions on results.

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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