Introduction to Machine Learning Week 7 NPTEL Assignment Answers 2025

NPTEL Introduction to Machine Learning Week 7 Assignment Answers 2024

1. Which of the following statement(s) regarding the evaluation of Machine Learning models is/are true?

• A model with a lower training loss will perform better on a test dataset.
• The train and test datasets should represent the underlying distribution of the data.
• To determine the variation in the performance of a learning algorithm, we generally use one training set and one test set.
• A learning algorithm can learn different parameter values if given different samples from the same distribution.

Answer :- b, d

2. Suppose we have a classification dataset comprising of 2 classes A and B with 100 and 50 samples respectively. Suppose we use stratified sampling to split the data into train and test sets. Which of the following train-test splits would be appropriate?

• Train- {A:80samples,B:30samples}, Test- {A:20samples,B:20samples}
• Train- {A:20samples,B:20samples}, Test- {A:80samples,B:30samples}
• Train- {A:80samples,B:40samples}, Test- {A:20samples,B:10samples}
• Train- {A:20samples,B:10samples}, Test- {A:80samples,B:40samples}

Answer :- c

3. Suppose we are performing cross-validation on a multiclass classification dataset with N data points. Which of the following statement(s) is/are correct?

• In k-fold cross validation, each fold should have a class-wise proportion similar to the given dataset.
• In k-fold cross-validation, we train one model and evaluate it on the k different test sets.
• In LOOCV, we train N different models, using (N-1) data points for training each model.
• In LOOCV, we can use the same test data to evaluate all the trained models.

Answer :- a, c, 

7. Consider the following statements.
Statement P : Boosting takes multiple weak classifiers and combines them into a strong classifier.
Statement Q : Boosting assigns equal weights to the predictions of all the weak classifiers, resulting in a high overall performance.

• P is True. Q is True. Q is the correct explanation for A.
• P is True. Q is True. Q is not the correct explanation for A.
• P is True. Q is False.
• Both P and Q are False.

Answer :- c

8. Which of the following statement(s) about ensemble methods is/are correct?

• The individual classifiers in bagging cannot be trained parallelly.
• The individual classifiers in boosting cannot be trained parallelly.
• A committee machine can consist of different kinds of classifiers like SVM, decision trees and logistic regression.
• Bagging further increases the variance of an unstable classifier.

Answer :- a, c