Naive Bayes classifier in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

How does Naive Bayes handle feature independence?

Naive Bayes assumes that features are independent given the class. What does this mean in simple terms?

A Features depend on each other to predict the class.

B Features are combined into one before classification.

C Features are ignored if they are correlated.

D Each feature affects the class independently without influencing other features.

❓ Predict Output

intermediate

Output of Naive Bayes prediction code

What is the predicted class output by this code snippet?

ML Python

from sklearn.naive_bayes import GaussianNB
import numpy as np

X_train = np.array([[1, 20], [2, 21], [3, 22], [4, 23]])
y_train = np.array([0, 0, 1, 1])
model = GaussianNB()
model.fit(X_train, y_train)

X_test = np.array([[1.5, 20.5]])
prediction = model.predict(X_test)
print(prediction[0])

A 0

B 1

C 2

D Error

❓ Hyperparameter

advanced

Choosing the smoothing parameter in Naive Bayes

In Multinomial Naive Bayes, what effect does increasing the smoothing parameter alpha have on the model?

A It removes features with low frequency from the dataset.

B It reduces the effect of zero-frequency problems by adding more weight to unseen features.

C It decreases training time by skipping some features.

D It increases the model complexity by adding more features.

❓ Metrics

advanced

Evaluating Naive Bayes with imbalanced data

You trained a Naive Bayes classifier on imbalanced classes. Which metric is best to evaluate its performance fairly?

A F1-score

B Accuracy

C Precision

D Training loss

🔧 Debug

expert

Why does this Naive Bayes code raise an error?

What error does this code raise and why?

ML Python

from sklearn.naive_bayes import GaussianNB

X_train = [[1, 2], [3, 4]]
y_train = [0, 1, 0]
model = GaussianNB()
model.fit(X_train, y_train)

A IndexError: list index out of range

B TypeError: Unsupported operand type(s) for +: 'int' and 'str'

C ValueError: Found input variables with inconsistent numbers of samples

D No error, code runs successfully