# NPTEL Introduction to Machine Learning Assignment 7 Answers 2022 Are you looking for the Answers to NPTEL Introduction to Machine Learning Assignment 7 – IIT Madras? This article will help you with the answer to the National Programme on Technology Enhanced Learning (NPTEL) Course “NPTEL Introduction to Machine Learning Assignment 7

## What is Introduction to Machine Learning?

With the increased availability of data from varied sources there has been increasing attention paid to the various data driven disciplines such as analytics and machine learning. In this course we intend to introduce some of the basic concepts of machine learning from a mathematically well motivated perspective. We will cover the different learning paradigms and some of the more popular algorithms and architectures used in each of these paradigms.

## CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of the average of best 8 assignments out of the total 12 assignments given in the course.
Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF THE AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

Below you can find the answers for NPTEL Introduction to Machine Learning Assignment 7

## NPTEL Introduction to Machine Learning Assignment 7 Answers:-

Q1. In LOO Cross Validation, you get K estimators. (excluding the final estimator that may be an ensemble of these K estimators)
If size of dataset = N, K =?

Q2. Given the following information

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Q3. To plot ROC curve, you first order the data points in _____ order of their likelihood of being positive.

Q4. Which of the following are true?TP – True Positive, TN True Negative, FP – False Positive, FN – False Negative

Q5. Consider the following two statements:

A: In bagging, the estimators can be trained parallely. B: Each estimator in bagging uses the same algorithm.

Q6. For a binary classification problem, consider the two statements below:

A: A classifier with AUC=0 is the least useful classifier.
B: A classifier with AUC=0.5 is the least useful classifier.

Q7. The relationship between their recall is:

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Q8. True/False: Model A is equivalent to a random model based on its confusion matrix.

Q9. Consider the following two statements:

A: The estimators in Boosting can be trained in parallel. B: Boosting is simply Bagging with a different sample distribution.