The IIT Madras BS Machine Learning Practice (MLP) End Term paper sat on 31 Aug 2025, in the May 2025 term, set 1: 30 questions for 54 marks in 180 minutes. Every question is below with its answer. Take it as a timed mock test to be marked, or read it through first.
THIS IS QUESTION PAPER FOR THE SUBJECT "DIPLOMA LEVEL : MACHINE LEARNING PRACTICE (COMPUTER BASED EXAM)" ARE YOU SURE YOU HAVE TO WRITE EXAM FOR THIS SUBJECT? CROSS CHECK YOUR HALL TICKET TO CONFIRM THE SUBJECTS TO BE WRITTEN. (IF IT IS NOT THE CORRECT SUBJECT, PLS CHECK THE SECTION AT THE TOP FOR THE SUBJECTS REGISTERED BY YOU)
YES
NO
Correct answer
YES
10
50
100
500
Correct answer
10
Set ‘warm start=True’
Combine training data from different training sessions
Set ‘retain parameters=True’
This problem can not be solved.
Correct answer
Set ‘warm start=True’
Correct answer
(200, 200, 3)
(40000, )
(200, 200, 1)
(40000, 3)
Correct answer
(40000, )
Correct answer
Which of the following statements about the Augmented Dickey-Fuller (ADF) test is correct?
The test is used to detect seasonality in a time series
A low p-value (e.g., < 0.05) indicates the time series is non-stationary
The test’s null hypothesis is that the time series is stationary
A low p-value (e.g., < 0.05) indicates the time series is stationary
Correct answer
A low p-value (e.g., < 0.05) indicates the time series is stationary
How to change the loss function in SGDRegressor to Huber loss?
Correct answer
liblinear
lbfgs
saga
newton-cg
Correct answer
saga
Correct answer
Which of the following best describes the components of a time series in a classical multiplicative decomposition model?
The series is a product of a trend component and an error component only
The series is a sum of a trend component, a seasonal component, and acyclical component
The series is a product of a trend component, a seasonal component, and anerror component
The series is a sum of a trend component, a seasonal component, and a errorcomponent
Correct answer
The series is a product of a trend component, a seasonal component, and anerror component
Correct answers
Saves computation time by reusing results of expensive transformations wheninputs and parameters have not changed.
Helps speed up repeated runs of cross-validation involving the sametransformation steps.
Reduces CPU usage by running transformations in parallel threadsautomatically.
Improves efficiency in hyperparameter search by avoiding redundantcomputations in pipeline steps.
Guarantees better generalization performance on the test set.
Correct answers
Saves computation time by reusing results of expensive transformations wheninputs and parameters have not changed.
Helps speed up repeated runs of cross-validation involving the sametransformation steps.
Improves efficiency in hyperparameter search by avoiding redundantcomputations in pipeline steps.
Correct answers
A medical diagnosis model is used to detect a rare disease that occurs in 0.5% of the population. A model is trained on this data, which of the following evaluation metrics are suitable for measuring effectiveness of this model:
accuracy
precision
recall
F-1 score
Correct answers
precision
recall
F-1 score
The elbow method involves plotting the number of clusters against the inertiaand identifying a point where the decrease in inertia slows down.
The elbow point represents the cluster count beyond which additional clustersdo not significantly improve the model fit.
The metric used in the elbow method is the silhouette score.
The elbow point is always the smallest number of clusters in the plot.
The elbow method may sometimes be inconclusive if no clear bend is visible inthe curve.
Correct answers
The elbow method involves plotting the number of clusters against the inertiaand identifying a point where the decrease in inertia slows down.
The elbow point represents the cluster count beyond which additional clustersdo not significantly improve the model fit.
The elbow method may sometimes be inconclusive if no clear bend is visible inthe curve.
A written answer, not marked automatically.
A written answer, not marked automatically.
Based on the above data, answer the given subquestions.
Correct answers
Based on the above data, answer the given subquestions.
Correct answer
Based on the above data, answer the given subquestions.
Correct answers
Based on the above data, answer the given subquestions.
select the appropriate values for A and B in the loss parameter to ensure that all model fits execute successfully.
Correct answer
Based on the above data, answer the given subquestions.
Correct answers
Based on the above data, answer the given subquestions.
A written answer, not marked automatically.
Based on the above data, answer the given subquestions.
Calculate and enter the value of X (truncated to two decimal places).
A written answer, not marked automatically.
Based on the above data, answer the given subquestions.
Calculate and enter the value of Y (truncated to two decimal places).
A written answer, not marked automatically.
Based on the above data, answer the given subquestions.
A written answer, not marked automatically.
Based on the above data, answer the given subquestions.
A written answer, not marked automatically.
Based on the above data, answer the given subquestions.
A written answer, not marked automatically.
Based on the above data, answer the given subquestions.
A written answer, not marked automatically.
