Text feature basics (CountVectorizer, TF-IDF) in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does CountVectorizer do in text processing?

CountVectorizer converts a collection of text documents into a matrix of token counts. It counts how many times each word appears in each document.

beginner

Explain TF-IDF in simple terms.

TF-IDF stands for Term Frequency-Inverse Document Frequency. It measures how important a word is to a document compared to all documents, giving higher scores to words that appear often in one document but rarely in others.

intermediate

Why use TF-IDF instead of just counting words?

Because some words like 'the' or 'and' appear in almost every document, counting them doesn't help. TF-IDF reduces the weight of common words and highlights unique words that better describe the document.

beginner

What is the output format of CountVectorizer and TF-IDF Vectorizer?

Both output a matrix where rows represent documents and columns represent words (features). Each cell contains either the count of the word (CountVectorizer) or the TF-IDF score (TF-IDF Vectorizer).

intermediate

How does CountVectorizer handle different words like 'run' and 'running'?

By default, CountVectorizer treats 'run' and 'running' as different words. To group them, you can use techniques like stemming or lemmatization before vectorizing.

What does CountVectorizer count in text data?

A Number of characters in each document

B Importance of words across all documents

C Number of times each word appears in each document

D Number of sentences in each document

CountVectorizer counts how many times each word appears in each document, creating a matrix of word counts.

What does TF-IDF help to identify in text data?

A Words that are important and unique to a document

B Length of each sentence

C Total number of words in a document

D Common words that appear in all documents

TF-IDF highlights words that are important and unique to a document by reducing the weight of common words.

Which of these is a limitation of CountVectorizer without preprocessing?

A It normalizes word forms

B It groups similar words automatically

C It removes stop words by default

D It ignores word order

CountVectorizer ignores word order and treats each word separately unless preprocessing like stemming is applied.

What is the shape of the output matrix from CountVectorizer for 100 documents and 500 unique words?

A 500 x 100

B 100 x 500

C 100 x 100

D 500 x 500

The output matrix has rows as documents and columns as unique words, so 100 documents by 500 words.

Which step can improve CountVectorizer results by grouping word forms?

A Stemming or Lemmatization

B Tokenization

C Stop word removal

D Lowercasing

Stemming or lemmatization groups different forms of a word (like 'run' and 'running') into one base form.

Describe how CountVectorizer transforms text data into numbers.

Think about counting words in each document and organizing them in a table.

Explain why TF-IDF is useful compared to simple word counts.

Consider how common words like 'the' are treated differently.

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