What is the purpose of the tol parameter in the fit method of the stochastic regressor? from sklearn.linear_model import SGDRegressor from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error from sklearn.datasets import load_breast_cancer X,y = load_breast_cancer(return_X_y=True) model = SGDRegressor(early_stopping=True, validation_fraction=0.2, tol=0.001, n_iter_no_change=5) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) model.fit(X_train, y_train) y_pred = model.predict(X_test) mse = mean_squared_error(y_test, y_pred)

(A) It specifies the tolerance level for early stopping based on the change in the validation error.

(B) It controls the learning rate of the stochastic regressor during training.

(C) It determines the maximum number of iterations for the training process.

(D) It defines the fraction of the validation set used for early stopping.

Consider the following code: import numpy as np from sklearn.linear_model import LinearRegression X = np.array([[1, 1], [1, 2], [2, 2], [2, 3], [2, 1], [3, 3]]) # y = 1 x_0 + 2 x_1 + 3 y = np.dot(X, np.array([1, 2])) + 3 reg1 = LinearRegression(fit_intercept = False).fit(X, y) s1 = reg1.score(X, y) reg2 = LinearRegression(fit_intercept = True).fit(X, y) s2 = reg2.score(X, y) Which of the following is more likely to be true?

(B) Option figure from the original question paper

(A) Option figure from the original question paper

(C) Option figure from the original question paper

Given the following code snippet, which statement is true regarding the use of LabelEncoder? from sklearn.preprocessing import LabelEncoder data = ["cat", "dog", "fish", "cat", "bird", "dog", "bird"] encoder = LabelEncoder() encoded_data = encoder.fit_transform(data) print(encoded_data)

(A) Option figure from the original question paper

(D) Option figure from the original question paper

(B) Option figure from the original question paper

(C) Option figure from the original question paper

Consider the given Table 1: Banking data for the given subquestions stored as pandas dataframe in variable df >>> import pandas as pd >>> df = pd.read_csv('dataset.csv') >>> print(df) | Age | Job | Marital | Education | Balance | Housing | Contact | |---|---|---|---|---|---|---| | 21 | unemployed | married | secondary | 77387 | no | telephone | | 49 | management | married | tertiary | 2037 | no | nan | | 72 | self-employed | married | tertiary | 132 | no | cellular | | 31 | blue-collar | married | secondary | 298 | yes | nan | | 28 | admin | single | secondary | 2831 | yes | nan | | 39 | technician | single | secondary | 15 | yes | cellular | | 32 | blue-collar | married | primary | 131 | yes | nan | | 59 | management | married | tertiary | 5314 | no | cellular | | 27 | technician | single | secondary | 155 | yes | cellular | | 47 | blue-collar | married | primary | 259 | no | cellular | Table 1: Banking Data Based on the above data, answer the given subquestions. Choose the correct option to find the number of null values (represented as nan) present in the Contact column

(A) Option figure from the original question paper

(B) Option figure from the original question paper

(C) Option figure from the original question paper

(D) Option figure from the original question paper

Consider the given Table 1: Banking data for the given subquestions stored as pandas dataframe in variable df >>> import pandas as pd >>> df = pd.read_csv('dataset.csv') >>> print(df) | Age | Job | Marital | Education | Balance | Housing | Contact | |---|---|---|---|---|---|---| | 21 | unemployed | married | secondary | 77387 | no | telephone | | 49 | management | married | tertiary | 2037 | no | nan | | 72 | self-employed | married | tertiary | 132 | no | cellular | | 31 | blue-collar | married | secondary | 298 | yes | nan | | 28 | admin | single | secondary | 2831 | yes | nan | | 39 | technician | single | secondary | 15 | yes | cellular | | 32 | blue-collar | married | primary | 131 | yes | nan | | 59 | management | married | tertiary | 5314 | no | cellular | | 27 | technician | single | secondary | 155 | yes | cellular | | 47 | blue-collar | married | primary | 259 | no | cellular | Table 1: Banking Data Based on the above data, answer the given subquestions. Which of the following options can be used to compute the median of the Balance column for each category in the Marital feature?

(A) Option figure from the original question paper

(D) Option figure from the original question paper

(B) Option figure from the original question paper

(C) Option figure from the original question paper

Consider the given Table 1: Banking data for the given subquestions stored as pandas dataframe in variable df >>> import pandas as pd >>> df = pd.read_csv('dataset.csv') >>> print(df) | Age | Job | Marital | Education | Balance | Housing | Contact | |---|---|---|---|---|---|---| | 21 | unemployed | married | secondary | 77387 | no | telephone | | 49 | management | married | tertiary | 2037 | no | nan | | 72 | self-employed | married | tertiary | 132 | no | cellular | | 31 | blue-collar | married | secondary | 298 | yes | nan | | 28 | admin | single | secondary | 2831 | yes | nan | | 39 | technician | single | secondary | 15 | yes | cellular | | 32 | blue-collar | married | primary | 131 | yes | nan | | 59 | management | married | tertiary | 5314 | no | cellular | | 27 | technician | single | secondary | 155 | yes | cellular | | 47 | blue-collar | married | primary | 259 | no | cellular | Table 1: Banking Data Based on the above data, answer the given subquestions. Figure from the original question paper

(D) Error: Datatype not matching

Consider the Following codeblock for the given subquestions. import pandas as pd import numpy as np from sklearn.preprocessing import OneHotEncoder, StandardScaler from sklearn.compose import ColumnTransformer data = {"fruits": ['apple','orange', 'banana', 'orange', 'apple'], "price": [10,20,5,20,10], 'color': ['red', 'orange', 'yellow', 'orange', 'red']} df = pd.DataFrame(data) transformers = [ ('Ohe', OneHotEncoder(), [0,2]), ('scaler', StandardScaler(), [1]) ] ct = ColumnTransformer(transformers = transformers) transformed_df = ct.fit_transform(df) Based on the above data, answer the given subquestions. Figure from the original question paper

(D) Option figure from the original question paper

(A) Option figure from the original question paper

(B) Option figure from the original question paper

(C) Option figure from the original question paper

(E) Option figure from the original question paper

January 2025 term · Machine Learning Practice · BSCS2008

Machine Learning Practice Quiz 1: 23 February 2025 (January 2025 term)

The IIT Madras BS Machine Learning Practice (MLP) Quiz 1 paper sat on 23 Feb 2025, in the January 2025 term: 20 questions for 40 marks in 120 minutes. Every question is below with its answer. Take it as a timed mock test to be marked, or read it through first.

Questions: 20
Marks: 40
Duration: 120 min
MCQ: 11
MSQ: 4
Numerical: 5

Take as mock test Practise with answers

Updated 25 Sept 2026

Official paper: IIT M DIPLOMA AN EXAM QDD2 23 Feb 2025 · No negative marking.

Question 1

+2 marksOne correct option

What is the primary risk of not performing a train-test split in a machine learning workflow?

A
The training time will increase.
B
The model might overfit the training data and perform poorly on new data.
C
The dataset may become unbalanced.
D
The model may fail to converge.

Show answer

Correct answer

B
The model might overfit the training data and perform poorly on new data.

Question 1 on its own page

Question 2

+2 marksOne correct option

Why is data preprocessing necessary before using it for model building?

A
The data may contain outliers or missing values due to errors in data collection.
B
Features may have different scales, affecting model performance.
C
The dataset may include non-numerical features that need to be converted to numerical representations.
D
All of these

Show answer

Correct answer

D
All of these

Question 2 on its own page

Question 3

+2 marksOne correct option

What is the purpose of the tol parameter in the fit method of the stochastic regressor?

python

1from sklearn.linear_model import SGDRegressor
2from sklearn.model_selection import train_test_split
3from sklearn.metrics import mean_squared_error
4from sklearn.datasets import load_breast_cancer
5
6X,y = load_breast_cancer(return_X_y=True)
7
8model = SGDRegressor(early_stopping=True,
9                     validation_fraction=0.2,
10                     tol=0.001,
11                     n_iter_no_change=5)
12
13X_train, X_test, y_train, y_test = train_test_split(X, y,
14                                                    test_size=0.2,
15                                                    random_state=42)
16
17model.fit(X_train, y_train)
18y_pred = model.predict(X_test)
19mse = mean_squared_error(y_test, y_pred)

A
It specifies the tolerance level for early stopping based on the change in the validation error.
B
It controls the learning rate of the stochastic regressor during training.
C
It determines the maximum number of iterations for the training process.
D
It defines the fraction of the validation set used for early stopping.

Show answer

Correct answer

A
It specifies the tolerance level for early stopping based on the change in the validation error.

Question 3 on its own page

Question 4

+2 marksOne correct option

Suppose we want to transform features in a dataset using polynomial transformation. The sklearn API provides the functionality in which of the following modules?

A
sklearn.dataset
B
sklearn.model_selection
C
sklearn.preprocessing
D
sklearn.featureSelection
E
sklearn.featureExtraction

Show answer

Correct answer

C
sklearn.preprocessing

Question 4 on its own pageAlso asked in Quiz 1 26 Feb 2023

Question 5

+3 marksOne correct option

For a dataset with 1000 data points and 100 features, the following code will generate how many models during execution?

Note: X is the feature matrix and y is the target vector.

python

1from sklearn.model_selection import cross_val_score
2from sklearn.model_selection import LeaveOneOut
3from sklearn.linear_model import linear_regression
4lin_reg = linear_regression()
5loocv = LeaveOneOut()
6score = cross_val_score(lin_reg, X, y, cv=loocv)

A
1000
B
100
C
99
D
999

Show answer

Correct answer

A
1000

Question 5 on its own pageAlso asked in End Term 1 Sept 2024

Question 6

+3 marksOne correct option

Consider the following code:

python

1import numpy as np
2from sklearn.linear_model import LinearRegression
3X = np.array([[1, 1], [1, 2], [2, 2], [2, 3], [2, 1], [3, 3]])
4# y = 1 * x_0 + 2 * x_1 + 3
5y = np.dot(X, np.array([1, 2])) + 3
6
7reg1 = LinearRegression(fit_intercept = False).fit(X, y)
8s1 = reg1.score(X, y)
9
10reg2 = LinearRegression(fit_intercept = True).fit(X, y)
11s2 = reg2.score(X, y)

Which of the following is more likely to be true?

Show answer

Correct answer

Question 6 on its own page

Question 7

+2 marksOne or more correct options

Given the following code snippet, which statement is true regarding the use of LabelEncoder?

python

1from sklearn.preprocessing import LabelEncoder
2
3data = ["cat", "dog", "fish", "cat", "bird", "dog", "bird"]
4
5encoder = LabelEncoder()
6encoded_data = encoder.fit_transform(data)
7
8print(encoded_data)

Select all that apply.

Show answer

Correct answers

Question 7 on its own page

Question 8

+2 marksOne or more correct options

Which of the following Evaluation metrics can be used in a regression problem?

Select all that apply.

A
Mean Squared Error
B
Accuracy
C
F1-Score
D
Mean Absolute Error
E
credit score
F
CGPA

Show answer

Correct answers

A
Mean Squared Error
D
Mean Absolute Error

Question 8 on its own pageAlso asked in Quiz 1 25 Feb 2024

Question 9

+2 marksNumerical answer

Show answer

Correct answer: 3.5

Question 9 on its own page

Question 10

+3 marksNumerical answer

For LinearRegression with equation $Y = W_0X_0+W_1X_1+W_2X_2$ and given that $W_2 = 2*W_1$ .

What will be the value of the $W_2$ for the below code if the model doesn't make any error?

(Write the answer correct upto one decimal)

Where $X_1$ and $X_2$ are column1 and column2 respectively and $W_1$ and $W_2$ are weights associated to the respected columns while fitting

python

1from sklearn.linear_model import LinearRegression
2X_train = [[1,2], [2,4], [3,6], [4,8]]
3y_train = [1,2,3,4]
4reg = LinearRegression(fit_intercept=False) #intercept=0
5reg.fit(X_train,y_train)
6print(reg.coef_[1])

Show answer

Correct answer: 0.4

Question 10 on its own page

Question 11

+2 marksOne or more correct options

Consider the given Table 1: Banking data for the given subquestions stored as pandas dataframe in variable df

python

1>>> import pandas as pd
2>>> df = pd.read_csv('dataset.csv')
3>>> print(df)

Age	Job	Marital	Education	Balance	Housing	Contact
21	unemployed	married	secondary	77387	no	telephone
49	management	married	tertiary	2037	no	nan
72	self-employed	married	tertiary	132	no	cellular
31	blue-collar	married	secondary	298	yes	nan
28	admin	single	secondary	2831	yes	nan
39	technician	single	secondary	15	yes	cellular
32	blue-collar	married	primary	131	yes	nan
59	management	married	tertiary	5314	no	cellular
27	technician	single	secondary	155	yes	cellular
47	blue-collar	married	primary	259	no	cellular

Table 1: Banking Data

Based on the above data, answer the given subquestions.

Which of the columns in the banking data are nominal features?

Select all that apply.

A
Job
B
Marital
C
Education
D
Housing
E
Contact

Show answer

Correct answers

A
Job
B
Marital
D
Housing
E
Contact

Question 11 on its own page

Question 12

+2 marksOne correct option

Consider the given Table 1: Banking data for the given subquestions stored as pandas dataframe in variable df

python

1>>> import pandas as pd
2>>> df = pd.read_csv('dataset.csv')
3>>> print(df)

Age	Job	Marital	Education	Balance	Housing	Contact
21	unemployed	married	secondary	77387	no	telephone
49	management	married	tertiary	2037	no	nan
72	self-employed	married	tertiary	132	no	cellular
31	blue-collar	married	secondary	298	yes	nan
28	admin	single	secondary	2831	yes	nan
39	technician	single	secondary	15	yes	cellular
32	blue-collar	married	primary	131	yes	nan
59	management	married	tertiary	5314	no	cellular
27	technician	single	secondary	155	yes	cellular
47	blue-collar	married	primary	259	no	cellular

Table 1: Banking Data

Based on the above data, answer the given subquestions.

Choose the correct option to find the number of null values (represented as nan) present in the Contact column

Show answer

Correct answer

Question 12 on its own page

Question 13

+2 marksOne correct option

Consider the given Table 1: Banking data for the given subquestions stored as pandas dataframe in variable df

python

1>>> import pandas as pd
2>>> df = pd.read_csv('dataset.csv')
3>>> print(df)

Age	Job	Marital	Education	Balance	Housing	Contact
21	unemployed	married	secondary	77387	no	telephone
49	management	married	tertiary	2037	no	nan
72	self-employed	married	tertiary	132	no	cellular
31	blue-collar	married	secondary	298	yes	nan
28	admin	single	secondary	2831	yes	nan
39	technician	single	secondary	15	yes	cellular
32	blue-collar	married	primary	131	yes	nan
59	management	married	tertiary	5314	no	cellular
27	technician	single	secondary	155	yes	cellular
47	blue-collar	married	primary	259	no	cellular

Table 1: Banking Data

Based on the above data, answer the given subquestions.

In Contact column which of the following statistical measures can be used to replace nan values?

A
Mean
B
Median
C
Mode
D
Variance

Show answer

Correct answer

C
Mode

Question 13 on its own page

Question 14

+2 marksOne or more correct options

Consider the given Table 1: Banking data for the given subquestions stored as pandas dataframe in variable df

python

1>>> import pandas as pd
2>>> df = pd.read_csv('dataset.csv')
3>>> print(df)

Age	Job	Marital	Education	Balance	Housing	Contact
21	unemployed	married	secondary	77387	no	telephone
49	management	married	tertiary	2037	no	nan
72	self-employed	married	tertiary	132	no	cellular
31	blue-collar	married	secondary	298	yes	nan
28	admin	single	secondary	2831	yes	nan
39	technician	single	secondary	15	yes	cellular
32	blue-collar	married	primary	131	yes	nan
59	management	married	tertiary	5314	no	cellular
27	technician	single	secondary	155	yes	cellular
47	blue-collar	married	primary	259	no	cellular

Table 1: Banking Data

Based on the above data, answer the given subquestions.

Which of the following options can be used to compute the median of the Balance column for each category in the Marital feature?

Select all that apply.

Show answer

Correct answers

Question 14 on its own page

Question 15

+1 markNumerical answer

Consider the given Table 1: Banking data for the given subquestions stored as pandas dataframe in variable df

python

1>>> import pandas as pd
2>>> df = pd.read_csv('dataset.csv')
3>>> print(df)

Age	Job	Marital	Education	Balance	Housing	Contact
21	unemployed	married	secondary	77387	no	telephone
49	management	married	tertiary	2037	no	nan
72	self-employed	married	tertiary	132	no	cellular
31	blue-collar	married	secondary	298	yes	nan
28	admin	single	secondary	2831	yes	nan
39	technician	single	secondary	15	yes	cellular
32	blue-collar	married	primary	131	yes	nan
59	management	married	tertiary	5314	no	cellular
27	technician	single	secondary	155	yes	cellular
47	blue-collar	married	primary	259	no	cellular

Table 1: Banking Data

Based on the above data, answer the given subquestions.

Show answer

Correct answer: 35.5

Question 15 on its own page

Question 16

+1 markOne correct option

Consider the given Table 1: Banking data for the given subquestions stored as pandas dataframe in variable df

python

1>>> import pandas as pd
2>>> df = pd.read_csv('dataset.csv')
3>>> print(df)

Age	Job	Marital	Education	Balance	Housing	Contact
21	unemployed	married	secondary	77387	no	telephone
49	management	married	tertiary	2037	no	nan
72	self-employed	married	tertiary	132	no	cellular
31	blue-collar	married	secondary	298	yes	nan
28	admin	single	secondary	2831	yes	nan
39	technician	single	secondary	15	yes	cellular
32	blue-collar	married	primary	131	yes	nan
59	management	married	tertiary	5314	no	cellular
27	technician	single	secondary	155	yes	cellular
47	blue-collar	married	primary	259	no	cellular

Table 1: Banking Data

Based on the above data, answer the given subquestions.

A
secondary
B
329
C
2859
D
Error: Datatype not matching

Show answer

Correct answer

C
2859

Question 16 on its own page

Question 17

+2 marksOne correct option

Consider the Following codeblock for the given subquestions.

python

1import pandas as pd
2import numpy as np
3from sklearn.preprocessing import OneHotEncoder, StandardScaler
4from sklearn.compose import ColumnTransformer
5
6data = {"fruits": ['apple','orange', 'banana', 'orange', 'apple'],
7        "price": [10,20,5,20,10],
8        'color': ['red', 'orange', 'yellow', 'orange', 'red']}
9df = pd.DataFrame(data)
10
11transformers = [
12    ('Ohe', OneHotEncoder(), [0,2]),
13    ('scaler', StandardScaler(), [1])
14]
15ct = ColumnTransformer(transformers = transformers)
16
17transformed_df = ct.fit_transform(df)

Based on the above data, answer the given subquestions.

A
(7,5)
B
(3,5)
C
(5,3)
D
(5,7)

Show answer

Correct answer

D
(5,7)

Question 17 on its own page

Question 18

+1 markNumerical answer

Consider the Following codeblock for the given subquestions.

python

1import pandas as pd
2import numpy as np
3from sklearn.preprocessing import OneHotEncoder, StandardScaler
4from sklearn.compose import ColumnTransformer
5
6data = {"fruits": ['apple','orange', 'banana', 'orange', 'apple'],
7        "price": [10,20,5,20,10],
8        'color': ['red', 'orange', 'yellow', 'orange', 'red']}
9df = pd.DataFrame(data)
10
11transformers = [
12    ('Ohe', OneHotEncoder(), [0,2]),
13    ('scaler', StandardScaler(), [1])
14]
15ct = ColumnTransformer(transformers = transformers)
16
17transformed_df = ct.fit_transform(df)

Based on the above data, answer the given subquestions.

Show answer

Correct answer: 13

Question 18 on its own page

Question 19

+2 marksNumerical answer

Consider the Following codeblock for the given subquestions.

python

1import pandas as pd
2import numpy as np
3from sklearn.preprocessing import OneHotEncoder, StandardScaler
4from sklearn.compose import ColumnTransformer
5
6data = {"fruits": ['apple','orange', 'banana', 'orange', 'apple'],
7        "price": [10,20,5,20,10],
8        'color': ['red', 'orange', 'yellow', 'orange', 'red']}
9df = pd.DataFrame(data)
10
11transformers = [
12    ('Ohe', OneHotEncoder(), [0,2]),
13    ('scaler', StandardScaler(), [1])
14]
15ct = ColumnTransformer(transformers = transformers)
16
17transformed_df = ct.fit_transform(df)

Based on the above data, answer the given subquestions.

Show answer

Correct answer: 36

Question 19 on its own page

Question 20

+2 marksOne correct option

Consider the Following codeblock for the given subquestions.

python

1import pandas as pd
2import numpy as np
3from sklearn.preprocessing import OneHotEncoder, StandardScaler
4from sklearn.compose import ColumnTransformer
5
6data = {"fruits": ['apple','orange', 'banana', 'orange', 'apple'],
7        "price": [10,20,5,20,10],
8        'color': ['red', 'orange', 'yellow', 'orange', 'red']}
9df = pd.DataFrame(data)
10
11transformers = [
12    ('Ohe', OneHotEncoder(), [0,2]),
13    ('scaler', StandardScaler(), [1])
14]
15ct = ColumnTransformer(transformers = transformers)
16
17transformed_df = ct.fit_transform(df)

Based on the above data, answer the given subquestions.

Show answer

Correct answer

Question 20 on its own page