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 · 3 Sept 2023 · May 2023 term · Set QPD1-S1

Question 4: Consider the following Python code snippet that demonstra…

Question 4

+2 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 4 of 34 in the IIT Madras BS Machine Learning Practice (MLP) End Term paper sat on 3 Sept 2023, in the May 2023 term (IIT M DIPLOMA ET1 EXAM QPD1 S2 03 Sep). It carries 2 marks.

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