You want to find 3 topics from a set of documents but also want to ignore very common words like 'the' and 'and'. Which combination of scikit-learn tools is best?

hard📝 Conceptual Q8 of 15

NLP - Topic Modeling

AUse CountVectorizer with stop_words='english' and then fit LDA with n_components=3

BUse TfidfVectorizer with n_components=3 directly for LDA

CUse CountVectorizer without stop words and set n_components=1 in LDA

DUse StandardScaler on raw text and then fit LDA with n_components=3

Step-by-Step Solution

Solution:

Step 1: Remove common stop words before LDA
CountVectorizer supports removing English stop words with stop_words='english'.
Step 2: Set number of topics in LDA
Set n_components=3 to find 3 topics. TfidfVectorizer is not recommended for LDA input.
Final Answer:
Use CountVectorizer with stop_words='english' and then fit LDA with n_components=3 -> Option A
Quick Check:
Stop words removal + n_components=3 = Use CountVectorizer with stop_words='english' and then fit LDA with n_components=3 [OK]

Quick Trick: Remove stop words with CountVectorizer before LDA [OK]

Common Mistakes:

MISTAKES

Using TfidfVectorizer directly for LDA
Not removing stop words
Setting n_components too low

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You want to find 3 topics from a set of documents but also want to ignore very common words like 'the' and 'and'. Which combination of scikit-learn tools is best?

Step 1: Remove common stop words before LDA

Step 2: Set number of topics in LDA

Final Answer:

Quick Check:

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