You're working on a binary classification task using the ‘MLPClassifier’ to predict whether a customer will make a purchase based on their browsing behavior. You're concerned about underfitting due to the complexity of the data. You decide to increase the ‘alpha’ parameter to control underfitting. The following code snippet shows the application of ‘MLPClassifier’: from sklearn.neural_network import MLPClassifier import numpy as np data = np.array([[10, 3], [20, 5], [5, 1], [15, 4], [8, 2]]) # Corresponding target labels (0: No Purchase, 1: Purchase) target = np.array([0, 1, 0, 1, 0]) # Initialize MLPClassifier with alpha parameter clf = MLPClassifier(alpha=0.001, random_state=42) clf.fit(data, target) # Predict class labels predicted_labels = clf.predict(data) By setting/changing the ‘alpha’ parameter to 0.01, how are you affecting the neural network model's behavior?

(C) Making the model more capable to learn more complex behaviour.

(A) Increasing the model’s complexity to fit the training data more closely.

(B) Adding a stronger regularization term to the loss function, discouraging complex models.

(D) Adjusting the learning rate to control convergence speed.

Machine Learning Practice · End Term · 24 Dec 2023 · September 2023 term · Set FDD1

Question 19: You're working on a binary classification task using the…

Question 19

+3 marksOne correct option

You're working on a binary classification task using the ‘MLPClassifier’ to predict whether a customer will make a purchase based on their browsing behavior. You're concerned about underfitting due to the complexity of the data. You decide to increase the ‘alpha’ parameter to control underfitting. The following code snippet shows the application of ‘MLPClassifier’:

python

1from sklearn.neural_network import MLPClassifier
2import numpy as np
3
4data = np.array([[10, 3], [20, 5], [5, 1], [15, 4], [8, 2]])
5
6# Corresponding target labels (0: No Purchase, 1: Purchase)
7target = np.array([0, 1, 0, 1, 0])
8
9# Initialize MLPClassifier with alpha parameter
10clf = MLPClassifier(alpha=0.001, random_state=42)
11clf.fit(data, target)
12
13# Predict class labels
14predicted_labels = clf.predict(data)

By setting/changing the ‘alpha’ parameter to 0.01, how are you affecting the neural network model's behavior?

A
Increasing the model’s complexity to fit the training data more closely.
B
Adding a stronger regularization term to the loss function, discouraging complex models.
C
Making the model more capable to learn more complex behaviour.
D
Adjusting the learning rate to control convergence speed.

Show answer

Correct answer

C
Making the model more capable to learn more complex behaviour.

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Question 19 of 38 in the IIT Madras BS Machine Learning Practice (MLP) End Term paper sat on 24 Dec 2023, in the September 2023 term (IIT M DIPLOMA FN EXAM FDD1 24 Dec 2023). It carries 3 marks.

Question 19: You're working on a binary classification task using the…

Question 19

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