The following code attempts to implement polynomial regression (degree 4) on the California housing dataset. Please select the appropriate option to complete the missing part correctly import numpy as np import pandas as pd from sklearn.datasets import fetch_california_housing from sklearn.model_selection import train_test_split from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import LinearRegression from sklearn.pipeline import Pipeline # Load dataset housing = fetch_california_housing() X, y = housing.data, housing.target # Split into training and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Define the polynomial regression model model = Pipeline([ ('poly', PolynomialFeatures(degree=4)), ('linear', LinearRegression()) ]) # Missing part # Make predictions y_pred = model.predict(X_test) Which of the following correctly fills the missing part blank while ensuring correct preprocessing and avoiding common pitfalls?

(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

Machine Learning Practice · End Term · 13 Apr 2025 · January 2025 term · Set QDD1

Question 7: The following code attempts to implement polynomial regre…

Question 7

+2 marksOne correct option

The following code attempts to implement polynomial regression (degree 4) on the California housing dataset. Please select the appropriate option to complete the missing part correctly

python

1import numpy as np
2import pandas as pd
3from sklearn.datasets import fetch_california_housing
4from sklearn.model_selection import train_test_split
5from sklearn.preprocessing import PolynomialFeatures
6from sklearn.linear_model import LinearRegression
7from sklearn.pipeline import Pipeline
8
9# Load dataset
10housing = fetch_california_housing()
11X, y = housing.data, housing.target
12
13# Split into training and test sets
14X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
15
16# Define the polynomial regression model
17model = Pipeline([
18    ('poly', PolynomialFeatures(degree=4)),
19    ('linear', LinearRegression())
20])
21
22# Missing part
23
24# Make predictions
25y_pred = model.predict(X_test)

Which of the following correctly fills the missing part blank while ensuring correct preprocessing and avoiding common pitfalls?

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Question 7 of 30 in the IIT Madras BS Machine Learning Practice (MLP) End Term paper sat on 13 Apr 2025, in the January 2025 term (IIT M DIPLOMA AN EXAM QDD3 13 Apr 2025). It carries 2 marks.

Question 7: The following code attempts to implement polynomial regre…

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