[Solved] Examine the following code snippet:from sklearn.metrics.pairwise import cosine_similarity vectors = [[1, 0], [0, 1]] sim = cosine_similarity(vectors[0], vectors[1]) print(sim)What is the main issue... | NLP

NLP - Text Similarity and Search

Examine the following code snippet:

from sklearn.metrics.pairwise import cosine_similarity
vectors = [[1, 0], [0, 1]]
sim = cosine_similarity(vectors[0], vectors[1])
print(sim)

What is the main issue causing this code to fail or produce an unexpected result?

AThe input vectors must be 2D arrays, but 1D lists were passed

Bcosine_similarity cannot handle vectors with zeros

CThe vectors must be normalized before computing similarity

Dcosine_similarity requires string inputs, not numeric lists

Step-by-Step Solution

Solution:

Step 1: Check input format
cosine_similarity expects 2D arrays (shape: [n_samples, n_features]).
Step 2: Identify issue
vectors[0] and vectors[1] are 1D lists, which causes an error or unexpected output.
Step 3: Correct usage
Wrap vectors in another list or convert to numpy arrays with shape (1, n_features).
Final Answer:
The input vectors must be 2D arrays, but 1D lists were passed -> Option A
Quick Check:
cosine_similarity requires 2D inputs [OK]

Quick Trick: cosine_similarity needs 2D arrays [OK]

Common Mistakes:

MISTAKES

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