MLS-C01 Exploratory Data Analysis Practice Question
A machine learning engineer is examining a dataset containing text reviews. They want to convert the text into numerical features for a model. During EDA, they notice that the word 'the' appears in almost every review, while words like 'excellent' appear rarely. Which of the following techniques should they use to reduce the impact of very common words?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Apply TF-IDF transformation.
TF-IDF transformation downweights common words (like 'the') and emphasizes rare but informative words (like 'excellent'). Option B (removing stopwords) is insufficient because it does not adjust for frequency beyond removing a predefined list; TF-IDF handles frequency weighting. Option C (word2vec embeddings) captures semantic relationships but does not specifically reduce the impact of common words. Option D (bag-of-words) does not perform any weighting, so common words dominate.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Apply TF-IDF transformation.
Why this is correct
TF-IDF downweights common words across documents, reducing their impact.
- ✗
Remove stopwords from the text.
Why it's wrong here
Removing stopwords only eliminates a fixed list of common words, but does not adjust for frequency-based weighting.
- ✗
Use word2vec embeddings.
Why it's wrong here
Word2vec embeddings capture context and semantics, not word frequency importance.
- ✗
Use a bag-of-words representation.
Why it's wrong here
Bag-of-words simply counts word occurrences without any weighting, so common words dominate.
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