Bag of Words and TF-IDF in ML Python - Model Metrics & Evaluation

Metrics & Evaluation - Bag of Words and TF-IDF

Which metric matters for Bag of Words and TF-IDF and WHY

When using Bag of Words or TF-IDF to turn text into numbers for models, the key metrics depend on the task. For classification, accuracy, precision, and recall matter because they show how well the model understands the text features. For ranking or search tasks, precision and recall help measure if the important words are captured well. These metrics tell us if the chosen text representation helps the model make good decisions.

Confusion Matrix Example

Imagine a spam email classifier using TF-IDF features. Here is a confusion matrix after testing 100 emails:

      |                 | Predicted Spam | Predicted Not Spam |
      |-----------------|----------------|--------------------|
      | Actual Spam     | True Positives (TP) = 40 | False Negatives (FN) = 5 |
      | Actual Not Spam | False Positives (FP) = 10 | True Negatives (TN) = 45 |
    

Total emails = 40 + 10 + 5 + 45 = 100

From this, we calculate:

• Precision = TP / (TP + FP) = 40 / (40 + 10) = 0.8
• Recall = TP / (TP + FN) = 40 / (40 + 5) = 0.89
• Accuracy = (TP + TN) / Total = (40 + 45) / 100 = 0.85

Precision vs Recall Tradeoff with Examples

Using Bag of Words or TF-IDF, sometimes the model finds many spam emails but also marks some good emails as spam (high recall, lower precision). Other times, it only marks very sure spam emails (high precision, lower recall).

Example 1: If you want to avoid missing spam (catch all spam), prioritize high recall. You accept some good emails wrongly marked as spam.

Example 2: If you want to avoid marking good emails as spam, prioritize high precision. You accept missing some spam emails.

Choosing Bag of Words or TF-IDF affects this tradeoff because TF-IDF downweights common words, helping precision by focusing on important words.

What Good vs Bad Metric Values Look Like

For text classification using Bag of Words or TF-IDF:

• Good: Precision and recall above 0.8 means the model finds most relevant texts and avoids many mistakes.
• Bad: Precision or recall below 0.5 means the model either misses many relevant texts or marks many irrelevant texts.
• Accuracy alone can be misleading if classes are imbalanced (e.g., spam is rare).

Common Metrics Pitfalls

• Accuracy Paradox: High accuracy can happen if most texts belong to one class, but the model fails on the minority class.
• Data Leakage: If the text features leak future information, metrics look better but model fails in real use.
• Overfitting: Very high training metrics but low test metrics means the model memorized training texts, not learned general patterns.
• Ignoring Class Imbalance: Not using precision and recall can hide poor performance on rare classes.

Self Check

Your spam detection model using TF-IDF has 98% accuracy but only 12% recall on spam emails. Is it good for production?

Answer: No. The model misses 88% of spam emails (low recall), so it fails to catch most spam even if overall accuracy looks high. This is bad for user experience.

Key Result

Precision and recall are key metrics for Bag of Words and TF-IDF to ensure models find relevant text without many mistakes.

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