Text feature basics (CountVectorizer, TF-IDF) in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a CountVectorizer instance.

ML Python

from sklearn.feature_extraction.text import [1]
vectorizer = [1]()

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

ALabelEncoder

BTfidfVectorizer

CCountVectorizer

DDictVectorizer

Attempts:

3 left

💡 Hint

Common Mistakes

Using TfidfVectorizer instead of CountVectorizer

Using LabelEncoder which is for labels, not text features

The CountVectorizer converts text into a matrix of token counts.

2fill in blank

medium

Complete the code to transform text data into a count matrix.

ML Python

texts = ['hello world', 'hello machine learning']
count_matrix = vectorizer.[1](texts)

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

Afit

Bpredict

Ctransform

Dfit_transform

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit only, which does not return the matrix

Using predict, which is not a method here

fit_transform learns the vocabulary and returns the count matrix in one step.

3fill in blank

hard

Fix the error in the code to create a TF-IDF vectorizer.

ML Python

from sklearn.feature_extraction.text import [1]
tfidf_vectorizer = [1]()

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

ACountVectorizer

BTfidfVectorizer

CTfidfTransformer

DHashingVectorizer

Attempts:

3 left

💡 Hint

Common Mistakes

Using TfidfTransformer which needs counts first

Using CountVectorizer which only counts words

TfidfVectorizer directly converts text to TF-IDF features.

4fill in blank

hard

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

ML Python

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

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

A1

B>

C>=

Dlen(word)

Attempts:

3 left

💡 Hint

Common Mistakes

Using len(word) as count instead of 1

Using >= instead of > causing inclusion of 3-letter words

We assign count 1 for each word longer than 3 letters using len(word) > 3.

5fill in blank

hard

Fill all three blanks to create a TF-IDF matrix from text data.

ML Python

from sklearn.feature_extraction.text import [1]
texts = ['machine learning', 'deep learning', 'machine intelligence']
tfidf = [2]()
matrix = tfidf.[3](texts)

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

ATfidfVectorizer

BTfidfTransformer

Cfit_transform

DCountVectorizer

Attempts:

3 left

💡 Hint

Common Mistakes

Using TfidfTransformer which requires count matrix input

Using CountVectorizer which does not compute TF-IDF

Use TfidfVectorizer to create the vectorizer and call fit_transform to get the TF-IDF matrix.

Practice

1. What does CountVectorizer do in text processing?

easy

A. Calculates the importance of words based on frequency and rarity

B. Counts how many times each word appears in the text

C. Removes stop words from the text

D. Converts text into lowercase only

Solution

1. Step 1: Understand CountVectorizer's role

   CountVectorizer transforms text into a matrix of token counts, counting word occurrences.

2. Step 2: Differentiate from TF-IDF

   Unlike TF-IDF, it does not weigh words by importance, only counts frequency.

3. Final Answer:

   Counts how many times each word appears in the text -> Option B

4. Quick Check:

   CountVectorizer = word counts [OK]

Hint: CountVectorizer counts words, TF-IDF scores importance [OK]

Common Mistakes:

• Confusing CountVectorizer with TF-IDF
• Thinking it removes stop words by default
• Assuming it normalizes text only

2. Which of the following is the correct way to import and create a CountVectorizer in Python?

easy

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

B. import CountVectorizer from sklearn.text
vectorizer = CountVectorizer()

C. from sklearn.text import CountVectorizer
vectorizer = CountVectorizer()

D. import CountVectorizer
vectorizer = CountVectorizer()

Solution

1. Step 1: Recall correct sklearn import path

   CountVectorizer is in sklearn.feature_extraction.text module.

2. Step 2: Check syntax correctness

   from sklearn.feature_extraction.text import CountVectorizer vectorizer = CountVectorizer() uses correct import and instantiation syntax.

3. Final Answer:

   from sklearn.feature_extraction.text import CountVectorizer vectorizer = CountVectorizer() -> Option A

4. Quick Check:

   Correct import path and syntax [OK]

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

Common Mistakes:

• Using wrong module path for import
• Incorrect import syntax (like import ... from ...)
• Forgetting to instantiate the class

3. What will be the output shape of the matrix after applying CountVectorizer on these two sentences?

sentences = ["I love cats", "Cats love me"]
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(sentences)
print(X.shape)

medium

A. (2, 4)

B. (2, 3)

C. (3, 2)

D. (4, 2)

Solution

1. Step 1: Count unique words in sentences

   Words are: 'i', 'love', 'cats', 'me' -> 4 unique words.

2. Step 2: Understand shape of output matrix

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

3. Final Answer:

   (2, 4) -> Option A

4. Quick Check:

   Rows = sentences, columns = unique words [OK]

Hint: Shape = (number of texts, unique words) [OK]

Common Mistakes:

• Mixing rows and columns in shape
• Counting duplicate words multiple times
• Ignoring case sensitivity (CountVectorizer lowercases by default)

4. Identify the error in this TF-IDF code snippet:

from sklearn.feature_extraction.text import TfidfVectorizer
texts = ["apple banana apple", "banana fruit"]
tfidf = TfidfVectorizer()
X = tfidf.fit_transform(texts)
print(tfidf.get_feature_names())

medium

A. fit_transform() should be called on texts as a string, not list

B. TfidfVectorizer() requires stop_words parameter

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

D. Import statement is incorrect

Solution

1. Step 1: Check method usage for feature names

   In recent sklearn versions, get_feature_names() is deprecated.

2. Step 2: Use updated method

   Use get_feature_names_out() instead to get feature names without error.

3. Final Answer:

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

4. Quick Check:

   Use get_feature_names_out() for TF-IDF features [OK]

Hint: Use get_feature_names_out() with TF-IDF [OK]

Common Mistakes:

• Using deprecated get_feature_names() method
• Passing wrong data type to fit_transform
• Incorrect import paths

5. You want to transform text data so that common words like 'the' and 'is' have less impact, but rare important words have higher scores. Which method should you use?

hard

A. One-hot encoding of words

B. CountVectorizer without stop words

C. Raw word counts from CountVectorizer

D. TF-IDF Vectorizer

Solution

1. Step 1: Understand the goal of reducing common word impact

   Common words appear frequently but carry less meaning, so their impact should be lowered.

2. Step 2: Identify method that weighs words by importance

   TF-IDF scores words higher if they are rare and important, reducing common word impact.

3. Final Answer:

   TF-IDF Vectorizer -> Option D

4. Quick Check:

   TF-IDF = importance weighting [OK]

Hint: Use TF-IDF to weigh rare words higher [OK]

Common Mistakes:

• Using raw counts which treat all words equally
• Assuming stop words removal alone solves importance
• Confusing one-hot encoding with frequency weighting