Identify the errors in the following code: Identify the errors in the following code:

(A) include_bias has to be kept true for polynomial features.

(B) MinMaxScalar should be used for simple LinearRegression Models

(C) model.fit_transform should be replace with model.fit

(D) model.predict should be replaced with model.predict_proba

What will typically happen if series_with_nans contains NaNs (null values) ? What will typically happen if series_with_nans contains NaNs (null values) ?

(A) statsmodels will automatically impute NaNs and fit silently.

(B) fit() will raise an error or drop NaNs depending on the estimator; user should handle NaNs beforehand.

(D) The model will only use the NaN values to estimate variance.

MLP End Term: 21 December 2025 (September 2025 term)

Question 1

+2 marksOne correct option

The adfuller() function in statsmodels is used to test:

A
Whether a time series has seasonal patterns
B
Whether a time series is stationary
C
Whether the data follows a normal distribution
D
Whether the variance of the data changes over time

Question 2

+1 markOne correct option

Identify the errors in the following code:

A
include_bias has to be kept true for polynomial features.
B
MinMaxScalar should be used for simple LinearRegression Models
C
model.fit_transform should be replace with model.fit
D
model.predict should be replaced with model.predict_proba

Question 3

+2 marksOne correct option

What will typically happen if series_with_nans contains NaNs (null values) ?

A
statsmodels will automatically impute NaNs and fit silently.
B
fit() will raise an error or drop NaNs depending on the estimator; user should handle NaNs beforehand.
C
NaNs are treated as zeros.
D
The model will only use the NaN values to estimate variance.

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More on the MLP End Term 21 Dec 2025 paper

The IIT Madras BS Machine Learning Practice (MLP) End Term paper sat on 21 Dec 2025, in the September 2025 term: 60 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 21 Dec 2025 at a glance
Term	September 2025 term
Subject	Machine Learning Practice
Course code	BSCS2008
Questions	60
Marks	100
Duration	180 min
MCQ	34
MSQ	9
Written	17
Official paper	Machine Learning Practice 18 Dec 25
Negative marking	No negative marking.
Updated	14 Sept 2026