The following code attempts to implement Ridge regression on the Boston housing dataset but contains an error. The missing part should be correctly filled in to avoid issues during training. import numpy as np import pandas as pd from sklearn.datasets import load_boston from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.linear_model import Ridge from sklearn.pipeline import Pipeline # Load dataset data = load_boston() X, y = data.data, data.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 Ridge regression model with standardization model = Pipeline([ ('scaler', StandardScaler()), ('ridge', Ridge(alpha=1.0)) ]) # Missing part # Make predictions y_pred = model.predict(X_test) Which of the following correctly fills the missing part while ensuring proper preprocessing?

(A) Option figure from the original question paper

(B) Option figure from the original question paper

(C) Option figure from the original question paper

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Machine Learning Practice · End Term · 13 Apr 2025 · January 2025 term · Set QDD3

Question 6: The following code attempts to implement Ridge regression…

Question 6

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The following code attempts to implement Ridge regression on the Boston housing dataset but contains an error. The missing part should be correctly filled in to avoid issues during training.

python

1import numpy as np
2import pandas as pd
3from sklearn.datasets import load_boston
4from sklearn.model_selection import train_test_split
5from sklearn.preprocessing import StandardScaler
6from sklearn.linear_model import Ridge
7from sklearn.pipeline import Pipeline
8
9# Load dataset
10data = load_boston()
11X, y = data.data, data.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 Ridge regression model with standardization
17model = Pipeline([
18    ('scaler', StandardScaler()),
19    ('ridge', Ridge(alpha=1.0))
20])
21
22# Missing part
23
24# Make predictions
25y_pred = model.predict(X_test)

Which of the following correctly fills the missing part while ensuring proper preprocessing?

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Question 6 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 6: The following code attempts to implement Ridge regression…

Question 6

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