Naive Bayes for text in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

NLP

from sklearn.naive_bayes import [1]

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ARandomForestClassifier

BMultinomialNB

CKNeighborsClassifier

DLinearRegression

Attempts:

3 left

💡 Hint

Common Mistakes

Importing a classifier not related to Naive Bayes.

Using regression models instead of classification.

The MultinomialNB class is the Naive Bayes classifier used for text classification tasks.

2fill in blank

medium

Complete the code to convert text data into numerical features using CountVectorizer.

NLP

from sklearn.feature_extraction.text import [1]
vectorizer = [2]()

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AStandardScaler

BTfidfTransformer

CLabelEncoder

DCountVectorizer

Attempts:

3 left

💡 Hint

Common Mistakes

Using TfidfTransformer without first vectorizing text.

Using LabelEncoder which is for labels, not text features.

CountVectorizer converts text documents into a matrix of token counts, which is needed before applying Naive Bayes.

3fill in blank

hard

Fix the error in the code to train the Naive Bayes model on vectorized text data.

NLP

model = MultinomialNB()
X_train_counts = vectorizer.fit_transform(texts)
model.[1](X_train_counts, labels)

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Afit

Btransform

Cpredict

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using transform() which is for feature extraction, not training.

Using predict() before training the model.

The fit method trains the model using the feature matrix and labels.

4fill in blank

hard

Fill both blanks to predict labels for new text data and convert them to a list.

NLP

X_new_counts = vectorizer.[1](new_texts)
predicted = model.[2](X_new_counts).tolist()

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Atransform

Bpredict

Cfit_transform

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit_transform on new data which retrains vectorizer incorrectly.

Using fit on new data which is wrong for prediction.

Use transform to convert new texts to features, then predict to get labels.

5fill in blank

hard

Fill all three blanks to create a dictionary of word counts for words longer than 3 characters.

NLP

word_counts = {word: [1] for word in text.split() if len(word) [2] 3 and word.isalpha() and word not in stopwords}
filtered_counts = {k: v for k, v in word_counts.items() if v [3] 1}

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Atext.count(word)

B>

C>=

Dlen(word)

Attempts:

3 left

💡 Hint

Common Mistakes

Using len(word) instead of counting occurrences.

Using wrong comparison operators causing empty results.

The dictionary counts each word's occurrences with text.count(word). We keep words longer than 3 characters (> 3) and filter counts greater or equal to 1 (>= 1).