Bag of Words (CountVectorizer) in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the CountVectorizer from sklearn.

NLP

from sklearn.feature_extraction.text import [1]

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ACountVectorizer

BTfidfVectorizer

CDictVectorizer

DFeatureHasher

Attempts:

3 left

💡 Hint

Common Mistakes

Importing TfidfVectorizer instead of CountVectorizer.

Using DictVectorizer which is for dictionaries, not text.

Trying to import from the wrong sklearn module.

CountVectorizer is the class used to convert text documents into a matrix of token counts.

2fill in blank

medium

Complete the code to create a CountVectorizer instance.

NLP

vectorizer = [1]()

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AStandardScaler

BCountVectorizer

CTfidfVectorizer

DLabelEncoder

Attempts:

3 left

💡 Hint

Common Mistakes

Using TfidfVectorizer which computes TF-IDF instead of counts.

Using StandardScaler which is for numeric data scaling.

Using LabelEncoder which is for categorical labels.

We create an instance of CountVectorizer to convert text to count vectors.

3fill in blank

hard

Fix the error in the code to transform the text data into count vectors.

NLP

X = vectorizer.[1](documents)

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Atransform

Btransform_fit

Cfit

Dfit_transform

Attempts:

3 left

💡 Hint

Common Mistakes

Using transform_fit which does not exist.

Using fit alone which does not transform data.

Using transform alone without fitting first.

fit_transform fits the vectorizer and transforms the documents in one step.

4fill in blank

hard

Fill in the blank to create a dictionary of word indices for words longer than 3 characters.

NLP

word_counts = {word: vectorizer.vocabulary_.get(word, 0) for word in vectorizer.get_feature_names_out() if len(word) [1] 3}

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A==

B<

C>

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' which selects shorter words.

Using '==' which selects words of length exactly 3.

Using '!=' which selects words not equal to length 3.

We want words longer than 3 characters, so we use '>' to filter.

5fill in blank

hard

Fill all three blanks to print the feature names, shape of X, and the count matrix as an array.

NLP

print('Features:', vectorizer.[1]())
print('Shape:', X.[2])
print('Array:\n', X.[3]())

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Aget_feature_names_out

Bshape

Ctoarray

Dtodense

Attempts:

3 left

💡 Hint

Common Mistakes

Using todense instead of toarray which returns a matrix, not array.

Using shape as a method instead of attribute.

Using get_feature_names which is deprecated.

get_feature_names_out returns feature names, shape gives matrix size, toarray converts sparse matrix to array.