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Which Python code correctly computes cosine similarity between two embedding vectors `vec1` and `vec2` using sklearn?

easy📝 Syntax Q3 of 15
NLP - Text Similarity and Search
Which Python code correctly computes cosine similarity between two embedding vectors `vec1` and `vec2` using sklearn?
Acosine_similarity(vec1, [vec2])
Bcosine_similarity([vec1], [vec2])
Ccosine_similarity(vec1.tolist(), vec2.tolist())
Dcosine_similarity(vec1, vec2)
Step-by-Step Solution
Solution:

  1. Step 1: Recall sklearn cosine_similarity input format

    It expects 2D arrays, so vectors must be wrapped in lists.

  2. Step 2: Check each option's input format

    Only cosine_similarity([vec1], [vec2]) wraps both vectors in lists correctly.

  3. Final Answer:

    cosine_similarity([vec1], [vec2]) -> Option B

  4. Quick Check:

    Correct sklearn input = 2D arrays [OK]
Quick Trick: Wrap vectors in lists for sklearn cosine_similarity [OK]
Common Mistakes:
MISTAKES
  • Passing 1D arrays directly causing errors
  • Converting vectors to lists unnecessarily
  • Mixing 1D and 2D inputs

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