You’re building a machine learning pipeline to preprocess data and train a model on a classification task. You decide to use a pipeline that includes data preprocessing and a support vector machine (SVM) classifier. The following code snippet demonstrates the pipeline creation and usage: from sklearn.pipeline import Pipeline from sklearn.svm import SVC from sklearn.preprocessing import StandardScaler import numpy as np # Simulated data (features: X, target: y) X = np.array([[2, 3], [5, 7], [8, 10]]) y = np.array([0, 1, 0]) # Create a pipeline with StandardScaler and SVM classifier pipeline = Pipeline([ ('scaler', StandardScaler()), ('svm', SVC())]) # Fit the pipeline on training data pipeline.fit(X, y) # Make predictions using the trained pipeline predictions = pipeline.predict(X) What is the purpose of using the pipeline in this code snippet?

(A) The pipeline combines multiple models for better model performance.

(B) The pipeline allows for simultaneous training of the scaler and classifier.

(C) The pipeline simplifies the code by encapsulating preprocessing and modeling steps.

(D) The pipeline ensures that only linear SVM can be used for this classification task.

MLP End Term: 3 September 2023, Set QPD1-S2 (May 2023 term)

Question 1

+2 marksNumerical answer

Question 2

+2 marksOne correct option

Given below a y_train list which consists of pizza’s ordered by the customers in a shop.

python

1y_train =  [['regular', 'veg'],
2            ['medium', 'veg'],
3            ['regular', 'non-veg'],
4            ['medium', 'non-veg']]

MultiLabelBinarizer from sklearn library has been used to convert the y_train into numbers, so which of the following option matches with the output using the following code ?

python

1from sklearn.preprocessing import MultiLabelBinarizer
2mlb = MultiLabelBinarizer(classes=['regular','medium', 'veg', 'non-veg'])
3print(mlb.fit_transform(y_train))

Question 3

+2 marksOne correct option

You’re building a machine learning pipeline to preprocess data and train a model on a classification task. You decide to use a pipeline that includes data preprocessing and a support vector machine (SVM) classifier.

The following code snippet demonstrates the pipeline creation and usage:

python

1from sklearn.pipeline import Pipeline
2from sklearn.svm import SVC
3from sklearn.preprocessing import StandardScaler
4import numpy as np
5
6# Simulated data (features: X, target: y)
7X = np.array([[2, 3], [5, 7], [8, 10]])
8y = np.array([0, 1, 0])
9
10# Create a pipeline with StandardScaler and SVM classifier
11pipeline = Pipeline([
12    ('scaler', StandardScaler()),
13    ('svm', SVC())])
14# Fit the pipeline on training data
15pipeline.fit(X, y)
16
17# Make predictions using the trained pipeline
18predictions = pipeline.predict(X)

What is the purpose of using the pipeline in this code snippet?

A
The pipeline combines multiple models for better model performance.
B
The pipeline allows for simultaneous training of the scaler and classifier.
C
The pipeline simplifies the code by encapsulating preprocessing and modeling steps.
D
The pipeline ensures that only linear SVM can be used for this classification task.

31 more questions in this paper

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More on the MLP End Term 3 Sept 2023 Set QPD1-S2 paper

The IIT Madras BS Machine Learning Practice (MLP) End Term paper sat on 3 Sept 2023, in the May 2023 term, set QPD1-S2: 34 questions for 100 marks in 180 minutes. The first 3 questions are below. Sign in with Google — it is free — to see the whole paper with its answers and explanations, in learning mode or as a timed mock test.

Feature	MLP End Term 3 Sept 2023 Set QPD1-S2 at a glance
Term	May 2023 term
Subject	Machine Learning Practice
Course code	BSCS2008
Questions	34
Marks	100
Duration	180 min
Numerical	4
MCQ	24
MSQ	6
Official paper	IIT M DIPLOMA ET1 EXAM QPD1 S2 03 Sep
Negative marking	No negative marking.
Updated	25 Sept 2026

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