Consider the following Python code snippet that demonstrates the use of GaussianNB from scikit-learn: from sklearn.naive_bayes import GaussianNB import numpy as np X = np.array([[1.0, 2.0], [2.5, 3.5], [3.0, 5.0]]) y = np.array([0, 1, 0]) classifier = GaussianNB() classifier.fit(X, y) new_data = np.array([[2.0, 3.0]]) predicted_proba = classifier.predict_proba(new_data) In the context of the code above, what information does the array predicted_proba contain?

(C) The posterior probabilities of the classes for the new data points.

(A) The predicted classes for the new data points.

(B) The decision boundary values for the classes.

(D) The likelihood estimates for the new data points.

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

Question 16: Consider the following Python code snippet that demonstr…

Question 16

+3 marksOne correct option

Consider the following Python code snippet that demonstrates the use of GaussianNB from scikit-learn:

python

1from sklearn.naive_bayes import GaussianNB
2import numpy as np
3
4X = np.array([[1.0, 2.0],
5              [2.5, 3.5],
6              [3.0, 5.0]])
7
8y = np.array([0, 1, 0])
9
10classifier = GaussianNB()
11classifier.fit(X, y)
12
13new_data = np.array([[2.0, 3.0]])
14
15predicted_proba = classifier.predict_proba(new_data)

In the context of the code above, what information does the array predicted_proba contain?

A
The predicted classes for the new data points.
B
The decision boundary values for the classes.
C
The posterior probabilities of the classes for the new data points.
D
The likelihood estimates for the new data points.

Show answer

Correct answer

C
The posterior probabilities of the classes for the new data points.

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Question 16 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 3 marks.

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