TF-IDF (TfidfVectorizer) in NLP - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of TF-IDF vectorization with simple corpus

What is the shape of the TF-IDF matrix produced by the following code?

NLP

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

A (3, 4)

B (4, 3)

C (4, 4)

D (3, 3)

💡 Hint

Remember, rows correspond to documents and columns to unique words.

The corpus has 3 documents and 4 unique words: 'apple', 'orange', 'banana', 'fruit'. So the TF-IDF matrix shape is (3, 4).

🧠 Conceptual

intermediate

Understanding IDF effect on common words

Which statement best describes the effect of IDF (Inverse Document Frequency) in TF-IDF vectorization?

A IDF only considers the length of each document.

B IDF increases the weight of words that appear in many documents.

C IDF decreases the weight of words that appear in many documents.

D IDF assigns equal weight to all words regardless of frequency.

💡 Hint

Think about how common words like 'the' or 'and' should be treated.

IDF reduces the weight of words that appear in many documents to highlight more unique words.

❓ Hyperparameter

advanced

Effect of max_df parameter in TfidfVectorizer

What is the effect of setting max_df=0.5 in TfidfVectorizer?

A It normalizes the TF-IDF vectors to have max value 0.5.

B It ignores words that appear in less than 50% of the documents.

C It limits the maximum number of features to 50.

D It ignores words that appear in more than 50% of the documents.

💡 Hint

max_df controls filtering of very common words.

max_df=0.5 means words appearing in more than half the documents are ignored as too common.

❓ Metrics

advanced

Interpreting cosine similarity with TF-IDF vectors

Given two TF-IDF vectors A and B, which cosine similarity value indicates the most similar documents?

A 1.0

B 0.0

C -1.0

D 0.5

💡 Hint

Cosine similarity ranges from -1 to 1.

Cosine similarity of 1 means vectors point in the same direction, indicating maximum similarity.

🔧 Debug

expert

Identifying error in TF-IDF vectorization code

What error will the following code raise when executed? from sklearn.feature_extraction.text import TfidfVectorizer corpus = ['data science', 'machine learning'] vectorizer = TfidfVectorizer(stop_words='english') X = vectorizer.fit_transform(corpus) print(X.toarray()) print(vectorizer.get_feature_names_out()) print(vectorizer.stop_words_) print(vectorizer.vocabulary_['science'])

A AttributeError because 'stop_words_' attribute does not exist

B No error, code runs successfully

C TypeError because stop_words parameter expects a list, not a string

D KeyError because 'science' is removed as a stop word

💡 Hint

Check if 'science' is considered a stop word and if it exists in vocabulary.

No error occurs. 'science' is not an English stopword, so it remains in the vocabulary. The stop_words_ attribute exists after fitting, and stop_words='english' is valid.

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