[Solved] Identify the error in this code snippet that uses LDA with scikit-learn: from sklearn.decomposition import LatentDirichletAllocation from sklearn.feature_extraction.... | NLP

NLP - Topic Modeling

Identify the error in this code snippet that uses LDA with scikit-learn:

from sklearn.decomposition import LatentDirichletAllocation
from sklearn.feature_extraction.text import CountVectorizer

docs = ["cat dog", "dog mouse", "cat mouse"]
vectorizer = CountVectorizer()
dtm = vectorizer.fit_transform(docs)
lda = LatentDirichletAllocation(n_components=2)
lda.fit_transform(dtm)
print(lda.components_)

Alda.fit_transform returns a matrix but the code ignores it

BCountVectorizer should be replaced with TfidfVectorizer

Clda.components_ attribute does not exist

Dn_components must be equal to number of documents

Step-by-Step Solution

Solution:

Step 1: Check usage of fit_transform
lda.fit_transform returns the topic distribution matrix, but the code does not store or use this output.
Step 2: Verify attribute and parameters
lda.components_ exists and n_components can be any positive integer. CountVectorizer is valid here.
Final Answer:
lda.fit_transform returns a matrix but the code ignores it -> Option A
Quick Check:
fit_transform output must be captured or used [OK]

Quick Trick: Always store fit_transform output to use topic distributions [OK]

Common Mistakes:

MISTAKES

Ignoring fit_transform output
Thinking components_ attribute is missing
Believing n_components must match document count

Master "Topic Modeling" in NLP

9 interactive learning modes - each teaches the same concept differently

Learn Why Deep Model Try Challenge Experiment Recall Metrics

More NLP Quizzes

Identify the error in this code snippet that uses LDA with scikit-learn:

Step 1: Check usage of fit_transform

Step 2: Verify attribute and parameters

Final Answer:

Quick Check:

Want More Practice?