Vocabulary size control in NLP - Model Metrics & Evaluation

Metrics & Evaluation - Vocabulary size control

Which metric matters for Vocabulary Size Control and WHY

When controlling vocabulary size in NLP models, the key metrics are model accuracy and out-of-vocabulary (OOV) rate. Accuracy shows how well the model understands text with the chosen vocabulary. OOV rate tells us how many words in new text are missing from the vocabulary. A smaller vocabulary reduces model size and speeds up training but can increase OOV rate, hurting accuracy. So, balancing these metrics helps find the best vocabulary size.

Confusion Matrix Example for Vocabulary Size Impact

    Suppose we classify text into positive or negative sentiment.

    Vocabulary size: 5,000 words
    Total samples: 100

    Confusion Matrix:
      Predicted Positive | Predicted Negative
    ------------------------------------------
    Actual Positive | 40 (TP)          | 10 (FN)
    Actual Negative | 5 (FP)           | 45 (TN)

    Precision = TP / (TP + FP) = 40 / (40 + 5) = 0.89
    Recall = TP / (TP + FN) = 40 / (40 + 10) = 0.80
    F1 Score = 2 * (0.89 * 0.80) / (0.89 + 0.80) = 0.84

    If vocabulary size shrinks to 2,000, OOV words increase, causing more errors:

    Confusion Matrix:
      Predicted Positive | Predicted Negative
    ------------------------------------------
    Actual Positive | 30 (TP)          | 20 (FN)
    Actual Negative | 10 (FP)          | 40 (TN)

    Precision = 30 / (30 + 10) = 0.75
    Recall = 30 / (30 + 20) = 0.60
    F1 Score = 2 * (0.75 * 0.60) / (0.75 + 0.60) = 0.67
    

Tradeoff: Vocabulary Size vs Model Performance

Imagine packing a suitcase for a trip. A big suitcase (large vocabulary) lets you bring many clothes (words), so you are ready for anything (better accuracy). But it is heavy and slow to carry (larger model, slower training).

A small suitcase (small vocabulary) is light and fast but may miss important clothes (words), so you might feel unprepared (higher OOV, lower accuracy).

In NLP, choosing vocabulary size balances model speed and memory against understanding new text well.

Good vs Bad Metric Values for Vocabulary Size Control

• Good: Low OOV rate (under 5%), high accuracy (above 85%), balanced precision and recall.
• Bad: High OOV rate (above 15%), low accuracy (below 70%), large gap between precision and recall indicating poor generalization.

Good values mean the vocabulary covers most words the model sees, helping it predict well. Bad values mean many words are unknown, causing mistakes.

Common Pitfalls in Vocabulary Size Metrics

• Ignoring OOV rate: High accuracy on training data can hide poor performance on new text with many unknown words.
• Overfitting vocabulary: Using too large vocabulary may memorize training words but fail on new words.
• Data leakage: Including test words in vocabulary inflates accuracy falsely.
• Accuracy paradox: High accuracy with small vocabulary may happen if data is unbalanced, but model misses rare words.

Self-Check Question

Your NLP model has 98% accuracy but a 20% OOV rate on new text. Is it good for production? Why or why not?

Answer: No, because a 20% OOV rate means many words are unknown to the model. Even with high accuracy on known words, the model will struggle with new or rare words, reducing real-world performance. You should reduce OOV by increasing vocabulary or using subword methods.

Key Result

Balancing vocabulary size reduces unknown words and improves model accuracy while keeping model efficient.

Practice

1. What is the main purpose of controlling vocabulary size in NLP models?

easy

A. To add more rare words to the dataset

B. To increase the number of training epochs

C. To limit the number of words the model uses

D. To make the model ignore stop words

Solution

1. Step 1: Understand vocabulary size control

   Vocabulary size control means setting a limit on how many unique words the model can use.

2. Step 2: Identify the main goal

   The goal is to reduce complexity and noise by ignoring very rare words, so the model focuses on common words.

3. Final Answer:

   To limit the number of words the model uses -> Option C

4. Quick Check:

   Vocabulary size control = limit words [OK]

Hint: Vocabulary size control means limiting words used [OK]

Common Mistakes:

• Thinking it increases training epochs
• Believing it adds rare words
• Confusing it with stop word removal

2. Which parameter in scikit-learn's CountVectorizer controls the vocabulary size?

easy

A. max_features

B. min_df

C. stop_words

D. ngram_range

Solution

1. Step 1: Recall CountVectorizer parameters

   CountVectorizer has parameters like max_features, min_df, stop_words, and ngram_range.

2. Step 2: Identify parameter for vocabulary size

   max_features sets the maximum number of words (features) to keep, controlling vocabulary size.

3. Final Answer:

   max_features -> Option A

4. Quick Check:

   max_features controls vocabulary size [OK]

Hint: max_features sets max vocabulary size in vectorizers [OK]

Common Mistakes:

• Choosing min_df which filters by document frequency
• Confusing stop_words with vocabulary size
• Thinking ngram_range controls vocabulary size

3. What will be the output vocabulary size after running this code?

from sklearn.feature_extraction.text import CountVectorizer
texts = ['apple banana apple', 'banana orange', 'apple orange orange']
vectorizer = CountVectorizer(max_features=2)
vectorizer.fit(texts)
vocab = vectorizer.get_feature_names_out()
print(len(vocab))

medium

A. 3

B. 2

C. 4

D. 1

Solution

1. Step 1: Understand max_features effect

   max_features=2 means the vectorizer keeps only the top 2 most frequent words.

2. Step 2: Count unique words and frequencies

   Words: apple(3), banana(2), orange(3). Top 2 are apple and orange.

3. Final Answer:

   2 -> Option B

4. Quick Check:

   max_features=2 means vocabulary size = 2 [OK]

Hint: max_features limits vocabulary count to given number [OK]

Common Mistakes:

• Counting all unique words ignoring max_features
• Assuming max_features is minimum count
• Confusing frequency with vocabulary size

4. Identify the error in this code snippet that tries to limit vocabulary size:

from sklearn.feature_extraction.text import CountVectorizer
texts = ['cat dog', 'dog mouse', 'cat mouse']
vectorizer = CountVectorizer(max_features='3')
vectorizer.fit(texts)
vocab = vectorizer.get_feature_names_out()
print(vocab)

medium

A. max_features should be an integer, not a string

B. fit() should be replaced with fit_transform()

C. get_feature_names_out() is deprecated

D. texts should be a numpy array

Solution

1. Step 1: Check max_features type

   max_features expects an integer, but '3' is a string, causing a type error.

2. Step 2: Confirm other parts are correct

   fit() works fine, get_feature_names_out() is current method, texts can be list.

3. Final Answer:

   max_features should be an integer, not a string -> Option A

4. Quick Check:

   max_features type must be int [OK]

Hint: max_features must be int, not string [OK]

Common Mistakes:

• Using string instead of integer for max_features
• Thinking fit_transform is required here
• Believing get_feature_names_out is deprecated

5. You want to build a text classifier but your dataset has 100,000 unique words. To speed up training and reduce noise, which approach best controls vocabulary size?

hard

A. Increase max_features to 200,000 to include more words

B. Use all 100,000 words to keep maximum information

C. Remove stop words only without limiting vocabulary size

D. Set max_features to a smaller number like 5000 in your vectorizer

Solution

1. Step 1: Understand problem with large vocabulary

   100,000 words is large and slows training; many words may be rare and noisy.

2. Step 2: Choose best vocabulary control method

   Setting max_features to a smaller number like 5000 keeps common words and speeds training.

3. Final Answer:

   Set max_features to a smaller number like 5000 in your vectorizer -> Option D

4. Quick Check:

   Limit vocabulary size to speed training [OK]

Hint: Limit vocabulary size to speed training and reduce noise [OK]

Common Mistakes:

• Using all words causing slow training
• Only removing stop words without size control
• Increasing max_features unnecessarily