Bag of Words and TF-IDF in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a Bag of Words model using CountVectorizer.

ML Python

from sklearn.feature_extraction.text import CountVectorizer

corpus = ['I love machine learning', 'Machine learning is fun']
vectorizer = CountVectorizer()
X = vectorizer.[1](corpus)
print(X.toarray())

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

Afit_transform

Btransform

Cfit

Dfit_predict

Attempts:

3 left

💡 Hint

Common Mistakes

Using only fit() which does not transform the data.

Using transform() before fitting the vectorizer.

The fit_transform method learns the vocabulary and transforms the text data into a Bag of Words matrix in one step.

2fill in blank

medium

Complete the code to create a TF-IDF matrix from the corpus.

ML Python

from sklearn.feature_extraction.text import TfidfVectorizer

corpus = ['Data science is awesome', 'I love data science']
vectorizer = TfidfVectorizer()
X = vectorizer.[1](corpus)
print(X.toarray())

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

Afit

Bfit_predict

Ctransform

Dfit_transform

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit() only, which does not transform the data.

Using transform() without fitting first.

The fit_transform method fits the TF-IDF vectorizer to the corpus and transforms the text into TF-IDF features.

3fill in blank

hard

Fix the error in the code to correctly get feature names from the CountVectorizer.

ML Python

from sklearn.feature_extraction.text import CountVectorizer

corpus = ['AI is the future', 'Future is AI']
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(corpus)
features = vectorizer.[1]()
print(features)

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

Afeature_names

Bget_feature_names

Cget_feature_names_out

Dfeatures_names

Attempts:

3 left

💡 Hint

Common Mistakes

Using deprecated method get_feature_names() which may cause warnings.

Trying to access attributes that do not exist.

The method get_feature_names_out() is the correct and current way to get feature names from a fitted CountVectorizer.

4fill in blank

hard

Fill both blanks to create a dictionary of word counts for words longer than 3 characters.

ML Python

words = ['data', 'science', 'is', 'fun']
word_counts = {word: [1] for word in words if len(word) [2] 3}
print(word_counts)

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

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Using the wrong comparison operator.

The dictionary comprehension counts the length of each word and includes only words longer than 3 characters.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary of words and their counts where count is greater than 1.

ML Python

word_freq = {'data': 2, 'science': 1, 'fun': 3}
filtered = { [1]: [2] for [3] in word_freq.items() if [2] > 1 }
print(filtered)

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

Aword

Bcount

Cfor word, count

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect variable names that don't match the unpacking.

Not filtering correctly by count.

The dictionary comprehension iterates over word-frequency pairs and keeps only those with count greater than 1.

Practice

1. What does the Bag of Words model primarily count in a text document?

easy

A. The order of words in the document

B. The frequency of each word appearing in the document

C. The sentiment of the document

D. The length of the document in characters

Solution

1. Step 1: Understand Bag of Words concept

   Bag of Words counts how many times each word appears, ignoring order.

2. Step 2: Identify what is counted

   It counts word frequency, not order, sentiment, or length.

3. Final Answer:

   The frequency of each word appearing in the document -> Option B

4. Quick Check:

   Bag of Words = word counts [OK]

Hint: Bag of Words counts words, not order or meaning [OK]

Common Mistakes:

• Thinking Bag of Words considers word order
• Confusing frequency with sentiment
• Assuming it measures document length

2. Which of the following is the correct way to create a Bag of Words vector using scikit-learn's CountVectorizer in Python?

easy

A. from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer()
matrix = vectorizer.fit(documents)

B. from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = CountVectorizer()
matrix = vectorizer.fit_transform(documents)

C. from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer()
matrix = vectorizer.transform(documents)

D. from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer()
matrix = vectorizer.fit_transform(documents)

Solution

1. Step 1: Import and initialize CountVectorizer

   Correct import is from sklearn.feature_extraction.text import CountVectorizer and then create an instance.

2. Step 2: Fit and transform documents

   Use fit_transform to learn vocabulary and create the matrix in one step.

3. Final Answer:

   from sklearn.feature_extraction.text import CountVectorizer vectorizer = CountVectorizer() matrix = vectorizer.fit_transform(documents) -> Option D

4. Quick Check:

   CountVectorizer + fit_transform = Bag of Words matrix [OK]

Hint: Use fit_transform to build and apply vectorizer in one step [OK]

Common Mistakes:

• Using transform without fit first
• Importing TfidfVectorizer instead of CountVectorizer
• Calling fit without transform or fit_transform

3. Given the following Python code using TF-IDF, what will be the shape of the output matrix?

from sklearn.feature_extraction.text import TfidfVectorizer
docs = ['cat dog', 'dog mouse', 'cat mouse mouse']
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(docs)
print(X.shape)

medium

A. (4, 3)

B. (3, 4)

C. (3, 3)

D. (3, 2)

Solution

1. Step 1: Count unique words across all documents

   The words are 'cat', 'dog', 'mouse'. 'mouse' appears twice but counts once. So unique words = 3.

2. Step 2: Check if TfidfVectorizer adds extra tokens

   TfidfVectorizer by default tokenizes and counts unique words. Here, it finds 3 unique words: 'cat', 'dog', 'mouse'. So matrix shape is (number of docs, number of unique words) = (3,3).

3. Step 3: Verify options

   (3, 3) is (3,3), matching our count. But (3, 4) is (3,4), which suggests 4 unique words. Let's check if any extra token is created.

4. Step 4: Check for default token pattern

   By default, TfidfVectorizer splits on word boundaries, so no extra tokens. So shape is (3,3).

5. Final Answer:

   (3, 3) -> Option C

6. Quick Check:

   Docs=3, unique words=3, shape=(3,3) [OK]

Hint: Shape = (documents, unique words) counted by vectorizer [OK]

Common Mistakes:

• Counting repeated words multiple times
• Confusing rows and columns in shape
• Assuming extra tokens are added automatically

4. The following code tries to create a TF-IDF matrix but raises an error. What is the main problem?

from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer()
docs = ['apple orange', 'banana apple']
X = vectorizer.transform(docs)
print(X.toarray())

medium

A. fit_transform should be used before transform to learn vocabulary

B. The documents list is empty

C. TfidfVectorizer cannot handle multiple documents

D. toarray() is not a valid method for sparse matrices

Solution

1. Step 1: Understand TfidfVectorizer usage

   Before calling transform, vectorizer must learn vocabulary using fit or fit_transform.

2. Step 2: Identify error cause

   Code calls transform directly without fitting, so vocabulary is unknown, causing error.

3. Final Answer:

   fit_transform should be used before transform to learn vocabulary -> Option A

4. Quick Check:

   fit_transform first, then transform [OK]

Hint: Always fit vectorizer before transform [OK]

Common Mistakes:

• Calling transform without fit
• Assuming toarray() is invalid (it is valid)
• Thinking TfidfVectorizer can't handle multiple docs

5. You have two documents: doc1 = 'apple apple orange' and doc2 = 'apple banana banana'. Using TF-IDF, which word will have the highest weight in doc2 and why?

hard

A. 'banana', because it appears frequently in doc2 but less in doc1

B. 'apple', because it appears in both documents

C. 'orange', because it appears only in doc1

D. 'apple', because it appears most frequently overall

Solution

1. Step 1: Understand TF-IDF weighting

   TF-IDF gives higher weight to words frequent in one document but rare across all documents.

2. Step 2: Analyze word frequencies

   'banana' appears twice in doc2 but not in doc1, so it is rare overall but frequent in doc2.

3. Step 3: Compare with other words

   'apple' appears in both docs, so its IDF is lower. 'orange' is not in doc2, so no weight there.

4. Final Answer:

   'banana', because it appears frequently in doc2 but less in doc1 -> Option A

5. Quick Check:

   TF-IDF favors unique frequent words [OK]

Hint: TF-IDF boosts words frequent in one doc but rare overall [OK]

Common Mistakes:

• Choosing common words across docs as highest weight
• Confusing frequency with uniqueness
• Ignoring that words not in doc have zero weight