Using sklearn's NMF with n_components=5 on input matrix X of shape (100, 1000), what will be the shape of the components_ attribute after fitting?

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NLP - Topic Modeling

Using sklearn's NMF with n_components=5 on input matrix X of shape (100, 1000), what will be the shape of the components_ attribute after fitting?

A(100, 1000)

B(100, 5)

C(5, 1000)

D(5, 100)

Step-by-Step Solution

Solution:

Step 1: Recall NMF output shapes
For input X (samples=100, features=1000) and n_components=5, NMF factorizes X ≈ W * H.
Step 2: Understand components_ attribute
components_ corresponds to matrix H with shape (n_components, n_features) = (5, 1000).
Final Answer:
(5, 1000) -> Option C
Quick Check:
Check sklearn NMF docs for attribute shapes [OK]

Quick Trick: components_ shape is (n_components, n_features) [OK]

Common Mistakes:

MISTAKES

Confusing components_ with W matrix shape
Swapping rows and columns in output shapes

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Using sklearn's NMF with n_components=5 on input matrix X of shape (100, 1000), what will be the shape of the components_ attribute after fitting?

Step 1: Recall NMF output shapes

Step 2: Understand components_ attribute

Final Answer:

Quick Check:

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