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.

Practice

1. What does the TfidfVectorizer primarily do in text processing?

easy

A. It converts text into numbers reflecting word importance.

B. It translates text into another language.

C. It removes all punctuation from the text.

D. It counts the total number of characters in text.

Solution

1. Step 1: Understand the purpose of TfidfVectorizer

   TfidfVectorizer transforms text data into numerical values that represent how important each word is in the text.

2. Step 2: Compare options with this purpose

   Only It converts text into numbers reflecting word importance. describes converting text into numbers that reflect word importance, which matches the function of TfidfVectorizer.

3. Final Answer:

   It converts text into numbers reflecting word importance. -> Option A

4. Quick Check:

   TF-IDF = word importance numbers [OK]

Hint: TF-IDF = numbers showing word importance in text [OK]

Common Mistakes:

• Confusing TF-IDF with translation or punctuation removal
• Thinking TF-IDF counts characters instead of words
• Assuming TF-IDF just counts word frequency without weighting

2. Which of the following is the correct way to import TfidfVectorizer from scikit-learn?

easy

A. from sklearn.feature_extraction.text import TfidfVectorizer

B. import TfidfVectorizer from sklearn.text

C. from sklearn.text import TfidfVectorizer

D. import TfidfVectorizer from sklearn.feature_extraction

Solution

1. Step 1: Recall the correct module for TfidfVectorizer

   TfidfVectorizer is located in sklearn.feature_extraction.text module.

2. Step 2: Match the correct import syntax

   The correct Python import syntax is: from sklearn.feature_extraction.text import TfidfVectorizer, which matches from sklearn.feature_extraction.text import TfidfVectorizer.

3. Final Answer:

   from sklearn.feature_extraction.text import TfidfVectorizer -> Option A

4. Quick Check:

   Correct import path = from sklearn.feature_extraction.text import TfidfVectorizer [OK]

Hint: Remember sklearn.feature_extraction.text for TfidfVectorizer import [OK]

Common Mistakes:

• Using wrong module names like sklearn.text
• Incorrect import syntax order
• Trying to import from sklearn.feature_extraction without .text

3. What will be the shape of the output matrix after applying TfidfVectorizer on 3 documents with 5 unique words total?

medium

A. (5, 5)

B. (5, 3)

C. (3, 3)

D. (3, 5)

Solution

1. Step 1: Understand TfidfVectorizer output shape

   The output is a matrix where rows represent documents and columns represent unique words (features).

2. Step 2: Apply to given numbers

   With 3 documents and 5 unique words, the shape is (3, 5) -- 3 rows and 5 columns.

3. Final Answer:

   (3, 5) -> Option D

4. Quick Check:

   Output shape = (documents, unique words) = (3, 5) [OK]

Hint: Rows = documents, columns = unique words in TF-IDF matrix [OK]

Common Mistakes:

• Swapping rows and columns in output shape
• Confusing number of documents with number of words
• Assuming square matrix regardless of input

4. Given this code snippet, what is the error?

from sklearn.feature_extraction.text import TfidfVectorizer
texts = ['apple orange', 'orange banana', 'banana apple']
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(texts)
print(X.shape)
print(vectorizer.get_feature_names())

medium

A. fit_transform() requires a list of integers, not strings

B. get_feature_names() is deprecated; should use get_feature_names_out()

C. TfidfVectorizer() needs a parameter specifying language

D. print(X.shape) will cause an error because X is not defined

Solution

1. Step 1: Check method usage for feature names

   In recent scikit-learn versions, get_feature_names() is deprecated and replaced by get_feature_names_out().

2. Step 2: Verify other code parts

   fit_transform() accepts list of strings, TfidfVectorizer() works without language parameter, and X is defined correctly.

3. Final Answer:

   get_feature_names() is deprecated; should use get_feature_names_out() -> Option B

4. Quick Check:

   Use get_feature_names_out() instead of deprecated get_feature_names() [OK]

Hint: Use get_feature_names_out() for feature names in new sklearn versions [OK]

Common Mistakes:

• Using deprecated get_feature_names() causing warnings or errors
• Thinking fit_transform() needs numeric input
• Assuming language parameter is mandatory

5. You want to ignore very common words like 'the' and 'is' when using TfidfVectorizer. Which parameter helps you do this effectively?

hard

A. lowercase=false

B. max_features=1000

C. stop_words='english'

D. norm=null

Solution

1. Step 1: Identify parameter for ignoring common words

   The stop_words parameter removes common words (stop words) like 'the', 'is', 'and'. Setting stop_words='english' removes English stop words.

2. Step 2: Check other parameters

   max_features limits number of features but doesn't remove stop words; lowercase controls case; norm controls normalization, none remove stop words.

3. Final Answer:

   stop_words='english' -> Option C

4. Quick Check:

   stop_words='english' removes common words [OK]

Hint: Use stop_words='english' to skip common words [OK]

Common Mistakes:

• Confusing max_features with stop words removal
• Not using stop_words parameter at all
• Thinking lowercase removes stop words