Document-term matrix in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a document-term matrix using CountVectorizer.

NLP

from sklearn.feature_extraction.text import CountVectorizer

docs = ['I love AI', 'AI loves me']
vectorizer = CountVectorizer()
dtm = vectorizer.[1](docs)
print(dtm.toarray())

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

Afit_transform

Btransform

Cfit

Dtoarray

Attempts:

3 left

💡 Hint

Common Mistakes

Using transform before fitting the vectorizer.

Calling fit without transforming the data.

The fit_transform method learns the vocabulary and transforms the documents into a document-term matrix in one step.

2fill in blank

medium

Complete the code to get the feature names (words) from the vectorizer.

NLP

from sklearn.feature_extraction.text import CountVectorizer

docs = ['Data science is fun']
vectorizer = CountVectorizer()
dtm = vectorizer.fit_transform(docs)
words = vectorizer.[1]()
print(words)

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

Afeatures

Bget_feature_names

Cvocabulary_

Dget_feature_names_out

Attempts:

3 left

💡 Hint

Common Mistakes

Using get_feature_names which is deprecated.

Trying to access vocabulary_ directly instead of using the method.

get_feature_names_out() returns the list of feature names (words) after fitting the vectorizer.

3fill in blank

hard

Fix the error in the code to correctly create a document-term matrix from the list of documents.

NLP

from sklearn.feature_extraction.text import CountVectorizer

docs = ['Machine learning', 'Learning machines']
vectorizer = CountVectorizer()
dtm = vectorizer.[1](docs)
print(dtm.toarray())

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

Atransform

Bfit_transform

Cfit

Dtoarray

Attempts:

3 left

💡 Hint

Common Mistakes

Using transform without fitting first.

Using fit without transforming.

fit_transform must be used to both learn the vocabulary and transform the documents into a matrix.

4fill in blank

hard

Fill both blanks to create a document-term matrix and get the feature names.

NLP

from sklearn.feature_extraction.text import CountVectorizer

docs = ['AI is amazing', 'Amazing AI']
vectorizer = CountVectorizer()
dtm = vectorizer.[1](docs)
features = vectorizer.[2]()
print(features)

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

Afit_transform

Btransform

Cget_feature_names_out

Dget_feature_names

Attempts:

3 left

💡 Hint

Common Mistakes

Using transform instead of fit_transform.

Using deprecated get_feature_names method.

Use fit_transform to create the matrix and get_feature_names_out to get the feature names.

5fill in blank

hard

Fill all three blanks to create a document-term matrix, get feature names, and print the matrix as an array.

NLP

from sklearn.feature_extraction.text import CountVectorizer

docs = ['Deep learning', 'Learning deep']
vectorizer = CountVectorizer()
dtm = vectorizer.[1](docs)
features = vectorizer.[2]()
print(dtm.[3]())

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

Afit_transform

Bget_feature_names_out

Ctoarray

Dtransform

Attempts:

3 left

💡 Hint

Common Mistakes

Using transform instead of fit_transform.

Using get_feature_names instead of get_feature_names_out.

Forgetting to convert the matrix to an array before printing.

fit_transform creates the matrix, get_feature_names_out gets the words, and toarray converts the matrix to a readable array.