Naive Bayes classifier in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the Naive Bayes classifier from scikit-learn.

ML Python

from sklearn.naive_bayes import [1]

Drag options to blanks, or click blank then click option'

AGaussianNB

BDecisionTreeClassifier

CKNeighborsClassifier

DRandomForestClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Importing a different classifier like DecisionTreeClassifier.

Confusing Naive Bayes with KNeighborsClassifier.

The Naive Bayes classifier for continuous data in scikit-learn is GaussianNB.

2fill in blank

medium

Complete the code to create a Gaussian Naive Bayes model instance.

ML Python

model = [1]()

Drag options to blanks, or click blank then click option'

ALogisticRegression

BSVC

CRandomForestClassifier

DGaussianNB

Attempts:

3 left

💡 Hint

Common Mistakes

Using a classifier like SVC or LogisticRegression instead of GaussianNB.

Forgetting to add parentheses to create an instance.

To create a Naive Bayes model, instantiate GaussianNB().

3fill in blank

hard

Fix the error in the code to train the Naive Bayes model on features X_train and labels y_train.

ML Python

model.[1](X_train, y_train)

Drag options to blanks, or click blank then click option'

Atransform

Bfit

Cpredict

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict instead of fit to train the model.

Using transform which is not for training.

The fit method trains the model on the training data.

4fill in blank

hard

Fill both blanks to predict labels for X_test and calculate accuracy score.

ML Python

predictions = model.[1](X_test)
accuracy = accuracy_score(y_test, [2])

Drag options to blanks, or click blank then click option'

Apredict

Bpredictions

Cfit

Dtransform

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit instead of predict for predictions.

Passing the wrong variable to accuracy_score.

Use predict to get predictions, then pass them to accuracy_score to compute accuracy.

5fill in blank

hard

Fill all three blanks to create a dictionary of class prior probabilities from the trained model.

ML Python

class_priors = {cls: prob for cls, prob in zip(model.[1], model.[2])}
print(class_priors[[3]])

Drag options to blanks, or click blank then click option'

Aclasses_

Bclass_prior_

C'spam'

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict instead of attributes for class labels or priors.

Using a variable name instead of a string for the class label in print.

classes_ holds class labels, class_prior_ holds prior probabilities, and 'spam' is an example class label to access its prior.

Practice

1. What is the main assumption made by the Naive Bayes classifier about the features in the data?

easy

A. Features must be numeric only

B. All features are independent of each other

C. Features are dependent on each other

D. Features must be categorical only

Solution

1. Step 1: Understand Naive Bayes assumption

   Naive Bayes assumes that each feature contributes independently to the outcome, ignoring any correlation between features.

2. Step 2: Identify the correct assumption

   This independence assumption simplifies calculations and makes the model 'naive'.

3. Final Answer:

   All features are independent of each other -> Option B

4. Quick Check:

   Feature independence [OK]

Hint: Naive means simple independence assumption [OK]

Common Mistakes:

• Thinking features must be numeric
• Assuming features depend on each other
• Confusing Naive Bayes with other classifiers

2. Which of the following is the correct way to import the Gaussian Naive Bayes classifier from scikit-learn in Python?

easy

A. from sklearn.naive_bayes import GaussianNB

B. import GaussianNB from sklearn.naive_bayes

C. from sklearn.naive_bayes import Gaussian

D. import naive_bayes.GaussianNB

Solution

1. Step 1: Recall correct import syntax in Python

   Python uses 'from module import class' syntax to import specific classes.

2. Step 2: Match correct class and module names

   The Gaussian Naive Bayes class is named GaussianNB and is in the sklearn.naive_bayes module.

3. Final Answer:

   from sklearn.naive_bayes import GaussianNB -> Option A

4. Quick Check:

   Correct import syntax [OK]

Hint: Use 'from module import class' for sklearn imports [OK]

Common Mistakes:

• Using wrong import order
• Misspelling class or module names
• Trying to import non-existent classes

3. Given the following Python code using GaussianNB, what will be the predicted class for the input [[1.5, 2.0]]?

from sklearn.naive_bayes import GaussianNB
import numpy as np

X = np.array([[1, 2], [2, 3], [3, 4], [6, 7], [7, 8], [8, 9]])
y = np.array([0, 0, 0, 1, 1, 1])
model = GaussianNB()
model.fit(X, y)
pred = model.predict([[1.5, 2.0]])
print(pred)

medium

A. [2]

B. [1]

C. [0]

D. Error

Solution

1. Step 1: Understand training data and labels

   Points near [1, 2], [2, 3], [3, 4] belong to class 0; points near [6, 7], [7, 8], [8, 9] belong to class 1.

2. Step 2: Predict class for [1.5, 2.0]

   This input is close to class 0 points, so GaussianNB predicts class 0.

3. Final Answer:

   [0] -> Option C

4. Quick Check:

   Nearby points class = 0 [OK]

Hint: Predict class near closest training points [OK]

Common Mistakes:

• Confusing class labels
• Expecting class 1 for distant points
• Assuming error without checking code

4. Consider this code snippet using MultinomialNB:

from sklearn.naive_bayes import MultinomialNB
X = [[1, 0, 2], [0, 1, 1]]
y = [0, 1]
model = MultinomialNB()
model.fit(X, y)
pred = model.predict([[1, -1, 2]])
print(pred)

What is the error in this code?

medium

A. Model.fit() requires numpy arrays, not lists

B. MultinomialNB cannot be used with integer data

C. Labels y must be strings, not integers

D. Negative values in input features are not allowed

Solution

1. Step 1: Check input feature values for MultinomialNB

   MultinomialNB expects non-negative feature counts; negative values cause errors.

2. Step 2: Identify negative value in prediction input

   The input [[1, -1, 2]] contains -1, which is invalid for MultinomialNB.

3. Final Answer:

   Negative values in input features are not allowed -> Option D

4. Quick Check:

   Negative features invalid [OK]

Hint: MultinomialNB needs non-negative counts [OK]

Common Mistakes:

• Thinking labels must be strings
• Assuming lists cause errors
• Using MultinomialNB with negative values

5. You have a dataset with text messages labeled as 'spam' or 'not spam'. Which Naive Bayes variant is best suited for this text classification task and why?

hard

A. MultinomialNB, because it models word counts effectively

B. GaussianNB, because it handles continuous numeric data well

C. BernoulliNB, because it models binary features like word presence

D. ComplementNB, because it is designed for imbalanced datasets

Solution

1. Step 1: Understand text data representation

   Text messages are often represented as word count vectors for classification.

2. Step 2: Match Naive Bayes variant to data type

   MultinomialNB works well with count data, modeling frequency of words in messages.

3. Final Answer:

   MultinomialNB, because it models word counts effectively -> Option A

4. Quick Check:

   Text counts use MultinomialNB [OK]

Hint: Use MultinomialNB for word count features [OK]

Common Mistakes:

• Choosing GaussianNB for text data
• Confusing BernoulliNB with MultinomialNB
• Ignoring data type when selecting model