Why text classification categorizes documents in NLP - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the library used for text classification.

NLP

from sklearn.[1] import MultinomialNB

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

Afeature_extraction

Bmetrics

Cnaive_bayes

Dmodel_selection

Attempts:

3 left

💡 Hint

Common Mistakes

Importing from the wrong module like feature_extraction or metrics.

Trying to import MultinomialNB directly from sklearn.

The MultinomialNB model is part of the naive_bayes module in scikit-learn, commonly used for text classification.

2fill in blank

medium

Complete the code to convert text documents into numbers for the model.

NLP

from sklearn.feature_extraction.text import {{BLANK_1 }}
vectorizer = [1]()

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ALabelEncoder

BCountVectorizer

CTfidfTransformer

DStandardScaler

Attempts:

3 left

💡 Hint

Common Mistakes

Using TfidfTransformer directly without first vectorizing text.

Using LabelEncoder which is for labels, not text features.

CountVectorizer converts text into a matrix of token counts, which is a common first step in text classification.

3fill in blank

hard

Fix the error in the code to train the text classification model.

NLP

model = MultinomialNB()
model.[1](X_train, y_train)

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Afit

Bpredict

Ctransform

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict instead of fit to train the model.

Using transform which is for data preprocessing.

The fit method trains the model using the training data and labels.

4fill in blank

hard

Fill both blanks to create a dictionary of word counts for documents with words longer than 3 letters.

NLP

word_counts = {word: [1] for word in document.split() if len(word) [2] 3}

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Adocument.count(word)

Blen(word)

C>

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using len(word) in the first blank which gives word length, not count.

Using '<=' instead of '>' in the condition.

This dictionary counts how many times each word appears if the word length is greater than 3.

5fill in blank

hard

Fill all three blanks to filter documents with label 'spam' and count words longer than 4 letters.

NLP

filtered = {doc: [1] for doc, label in dataset.items() if label == '[2]' and len(doc.split()[0]) [3] 4}

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Alen(doc.split()[0])

Bspam

C>

Ddoc.upper()

Attempts:

3 left

💡 Hint

Common Mistakes

Using len(doc.split()[0]) in the second blank instead of the label string.

Using '<' instead of '>' in the length comparison.

Not transforming the document text in the first blank.

This code filters documents labeled 'spam' and creates a dictionary with the document text in uppercase if the first word is longer than 4 letters.