Bag of Words (CountVectorizer) in NLP - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of CountVectorizer on simple text

What is the output of the following code snippet using CountVectorizer from scikit-learn?

NLP

from sklearn.feature_extraction.text import CountVectorizer
corpus = ['apple banana apple', 'banana orange']
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(corpus)
result = X.toarray()
vocab = vectorizer.get_feature_names_out()
print(vocab)
print(result)

A ['apple' 'orange' 'banana']\n[[2 0 1]\n [0 1 1]]

B ['banana' 'apple' 'orange']\n[[1 2 0]\n [1 0 1]]

C ['apple' 'banana' 'orange']\n[[2 1 0]\n [0 1 1]]

D ['apple' 'banana' 'orange']\n[[1 2 0]\n [0 1 1]]

💡 Hint

CountVectorizer sorts vocabulary alphabetically and counts word occurrences per document.

The CountVectorizer creates a vocabulary sorted alphabetically: ['apple', 'banana', 'orange']. It counts how many times each word appears in each document. The first document has 'apple' twice and 'banana' once, no 'orange'. The second document has 'banana' once and 'orange' once.

🧠 Conceptual

intermediate

Understanding vocabulary size in CountVectorizer

Given the corpus: ['cat dog', 'dog mouse', 'cat mouse dog'], what is the vocabulary size created by CountVectorizer with default settings?

A 3

B 5

C 4

D 2

💡 Hint

CountVectorizer creates one vocabulary word per unique token across all documents.

The unique words are 'cat', 'dog', and 'mouse'. So the vocabulary size is 3.

❓ Hyperparameter

advanced

Effect of stop_words parameter in CountVectorizer

What will be the vocabulary output of CountVectorizer when applied to ['the cat sat', 'the dog barked'] with stop_words='english'?

A ['the', 'cat', 'sat', 'dog', 'barked']

B ['barked', 'cat', 'dog', 'sat']

C ['cat', 'dog']

D ['the']

💡 Hint

The stop_words='english' removes common English words like 'the'.

Stop words like 'the' are removed. Remaining words are 'cat', 'sat', 'dog', 'barked'. Vocabulary is sorted alphabetically.

❓ Metrics

advanced

Calculating document frequency with CountVectorizer

Using CountVectorizer on ['apple apple banana', 'banana orange', 'apple orange orange'], what is the document frequency (number of documents containing the word) for 'apple'?

A 2

B 3

C 1

D 0

💡 Hint

Document frequency counts in how many documents the word appears at least once.

'apple' appears in the first and third documents, so document frequency is 2.

🔧 Debug

expert

Identifying error in CountVectorizer usage

What error will the following code raise? from sklearn.feature_extraction.text import CountVectorizer corpus = ['hello world', 123, 'hello'] vectorizer = CountVectorizer() X = vectorizer.fit_transform(corpus)

A AttributeError: 'int' object has no attribute 'lower'

B ValueError: empty vocabulary; perhaps the documents only contain stop words

C No error, code runs successfully

D TypeError: expected string or bytes-like object

💡 Hint

CountVectorizer expects all documents to be strings.

The corpus contains an integer 123, which causes CountVectorizer to raise a TypeError because it expects strings.

Practice

1. What does the Bag of Words model do in text processing?

easy

A. Counts how often each word appears in the text

B. Translates text into another language

C. Removes all punctuation from the text

D. Generates summaries of the text

Solution

1. Step 1: Understand Bag of Words purpose

   Bag of Words counts the frequency of each word in a text, ignoring order.

2. Step 2: Compare options to definition

   Only Counts how often each word appears in the text matches this description exactly.

3. Final Answer:

   Counts how often each word appears in the text -> Option A

4. Quick Check:

   Bag of Words = Counts words [OK]

Hint: Bag of Words counts words, not translates or summarizes [OK]

Common Mistakes:

• Confusing Bag of Words with translation
• Thinking it removes punctuation only
• Assuming it summarizes text

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

easy

A. import CountVectorizer from sklearn.feature_extraction

B. from sklearn.feature_extraction.text import CountVectorizer

C. from sklearn.text import CountVectorizer

D. import CountVectorizer from sklearn.text

Solution

1. Step 1: Recall correct import path

   CountVectorizer is in sklearn.feature_extraction.text module.

2. Step 2: Match options to correct syntax

   Only from sklearn.feature_extraction.text import CountVectorizer uses the correct 'from ... import ...' syntax and correct module path.

3. Final Answer:

   from sklearn.feature_extraction.text import CountVectorizer -> Option B

4. Quick Check:

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

Hint: CountVectorizer is in sklearn.feature_extraction.text [OK]

Common Mistakes:

• Using wrong module path
• Incorrect import syntax
• Trying to import from sklearn.text

3. What will be the output shape of the matrix after applying CountVectorizer on these two sentences:
['I love cats', 'Cats love me']?

medium

A. (3, 2)

B. (2, 3)

C. (4, 2)

D. (2, 4)

Solution

1. Step 1: Identify unique words

   Words are: 'I', 'love', 'cats', 'me' (case insensitive, 'Cats' and 'cats' same).

2. Step 2: Count sentences and features

   There are 2 sentences and 4 unique words, so matrix shape is (2, 4).

3. Final Answer:

   (2, 4) -> Option D

4. Quick Check:

   2 sentences, 4 words = (2, 4) [OK]

Hint: Count unique words and sentences for shape (rows, columns) [OK]

Common Mistakes:

• Counting words per sentence instead of unique words
• Mixing rows and columns in shape
• Ignoring case sensitivity

4. The following code throws an error. What is the mistake?

from sklearn.feature_extraction.text import CountVectorizer
texts = ['hello world', 'hello']
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(texts)
print(X.toarray())
print(vectorizer.get_feature_names())

medium

A. get_feature_names() is deprecated, should use get_feature_names_out()

B. fit_transform() should be fit_transform_text()

C. toarray() is not a method of X

D. CountVectorizer() needs a parameter for language

Solution

1. Step 1: Identify deprecated method

   get_feature_names() is deprecated in recent sklearn versions.

2. Step 2: Use correct method

   Replace get_feature_names() with get_feature_names_out() to fix error.

3. Final Answer:

   get_feature_names() is deprecated, should use get_feature_names_out() -> Option A

4. Quick Check:

   Use get_feature_names_out() not get_feature_names() [OK]

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

Common Mistakes:

• Thinking fit_transform() is wrong
• Assuming toarray() is invalid
• Believing CountVectorizer needs language parameter

5. You have a list of sentences with some words repeated many times. How can you use CountVectorizer to ignore words that appear in more than 50% of the sentences?

hard

A. Set min_df=0.5 to ignore frequent words

B. Use stop_words='english' to remove frequent words

C. Set the parameter max_df=0.5 when creating CountVectorizer

D. Set max_features=0.5 to limit word count

Solution

1. Step 1: Understand max_df parameter

   max_df=0.5 tells CountVectorizer to ignore words in more than 50% of documents.

2. Step 2: Compare other options

   min_df controls minimum frequency, stop_words removes common English words, max_features limits number of features, none ignore frequent words by percentage.

3. Final Answer:

   Set the parameter max_df=0.5 when creating CountVectorizer -> Option C

4. Quick Check:

   max_df filters frequent words by document frequency [OK]

Hint: Use max_df to exclude very common words [OK]

Common Mistakes:

• Confusing max_df with min_df
• Thinking stop_words removes all frequent words
• Using max_features to filter frequency