TF-IDF (TfidfVectorizer) in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the TfidfVectorizer from scikit-learn.

NLP

from sklearn.feature_extraction.text import [1]

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

ALabelEncoder

BCountVectorizer

CStandardScaler

DTfidfVectorizer

Attempts:

3 left

💡 Hint

Common Mistakes

Importing CountVectorizer instead of TfidfVectorizer

Importing unrelated classes like StandardScaler or LabelEncoder

The TfidfVectorizer is the correct class to import for computing TF-IDF features from text data.

2fill in blank

medium

Complete the code to create a TfidfVectorizer instance with English stop words removed.

NLP

vectorizer = TfidfVectorizer(stop_words=[1])

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

ANone

BTrue

C'english'

DFalse

Attempts:

3 left

💡 Hint

Common Mistakes

Using True or False instead of 'english'

Using None which means no stop words are removed

Setting stop_words='english' tells the vectorizer to remove common English stop words.

3fill in blank

hard

Fix the error in the code to transform the documents into TF-IDF features.

NLP

tfidf_matrix = vectorizer.[1](documents)

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

Afit_transform

Bfit

Ctransform_fit

Dfit_transformer

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit alone which does not transform data

Using non-existent methods like transform_fit or fit_transformer

The fit_transform method fits the vectorizer to the data and transforms it in one step.

4fill in blank

hard

Fill both blanks to get the feature names and convert the TF-IDF matrix to a dense array.

NLP

feature_names = vectorizer.[1]()
dense_matrix = tfidf_matrix.[2]()

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

Aget_feature_names_out

Btoarray

Cfit_transform

Dtransform

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit_transform instead of get_feature_names_out

Using transform instead of toarray

get_feature_names_out() returns the list of feature names (words). toarray() converts the sparse matrix to a dense numpy array.

5fill in blank

hard

Fill all three blanks to create a TfidfVectorizer with max 1000 features, fit and transform documents, and get feature names.

NLP

vectorizer = TfidfVectorizer(max_features=[1])
tfidf_matrix = vectorizer.[2](documents)
features = vectorizer.[3]()

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

A1000

Bfit_transform

Cget_feature_names_out

D500

Attempts:

3 left

💡 Hint

Common Mistakes

Using 500 instead of 1000 for max_features

Using fit instead of fit_transform

Using get_feature_names instead of get_feature_names_out

Setting max_features=1000 limits the vocabulary size. Then fit_transform fits and transforms the documents. Finally, get_feature_names_out() retrieves the feature names.