Bag of Words (CountVectorizer) in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the CountVectorizer from sklearn.

NLP

from sklearn.feature_extraction.text import [1]

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

ACountVectorizer

BTfidfVectorizer

CDictVectorizer

DFeatureHasher

Attempts:

3 left

💡 Hint

Common Mistakes

Importing TfidfVectorizer instead of CountVectorizer.

Using DictVectorizer which is for dictionaries, not text.

Trying to import from the wrong sklearn module.

CountVectorizer is the class used to convert text documents into a matrix of token counts.

2fill in blank

medium

Complete the code to create a CountVectorizer instance.

NLP

vectorizer = [1]()

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

AStandardScaler

BCountVectorizer

CTfidfVectorizer

DLabelEncoder

Attempts:

3 left

💡 Hint

Common Mistakes

Using TfidfVectorizer which computes TF-IDF instead of counts.

Using StandardScaler which is for numeric data scaling.

Using LabelEncoder which is for categorical labels.

We create an instance of CountVectorizer to convert text to count vectors.

3fill in blank

hard

Fix the error in the code to transform the text data into count vectors.

NLP

X = vectorizer.[1](documents)

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

Atransform

Btransform_fit

Cfit

Dfit_transform

Attempts:

3 left

💡 Hint

Common Mistakes

Using transform_fit which does not exist.

Using fit alone which does not transform data.

Using transform alone without fitting first.

fit_transform fits the vectorizer and transforms the documents in one step.

4fill in blank

hard

Fill in the blank to create a dictionary of word indices for words longer than 3 characters.

NLP

word_counts = {word: vectorizer.vocabulary_.get(word, 0) for word in vectorizer.get_feature_names_out() if len(word) [1] 3}

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

A==

B<

C>

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' which selects shorter words.

Using '==' which selects words of length exactly 3.

Using '!=' which selects words not equal to length 3.

We want words longer than 3 characters, so we use '>' to filter.

5fill in blank

hard

Fill all three blanks to print the feature names, shape of X, and the count matrix as an array.

NLP

print('Features:', vectorizer.[1]())
print('Shape:', X.[2])
print('Array:\n', X.[3]())

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

Aget_feature_names_out

Bshape

Ctoarray

Dtodense

Attempts:

3 left

💡 Hint

Common Mistakes

Using todense instead of toarray which returns a matrix, not array.

Using shape as a method instead of attribute.

Using get_feature_names which is deprecated.

get_feature_names_out returns feature names, shape gives matrix size, toarray converts sparse matrix to array.

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