MLS-C01 Modeling Practice Question

A data scientist is building a text classification model. The dataset contains 10,000 documents, each labeled with one of 5 categories. Which algorithm is most suitable for this task?

⚠ Common exam trap

The MLS-C01 exam often tests the distinction between supervised and unsupervised learning, leading candidates to mistakenly choose k-means clustering (an unsupervised method) for a labeled classification task, or to confuse PCA with a classification algorithm because it is used for feature reduction before modeling.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

Naive Bayes

Naive Bayes is highly suitable for text classification because it models the probability of each category given the document's word features using Bayes' theorem with a strong independence assumption. It performs well on high-dimensional sparse data like bag-of-words or TF-IDF representations, and it is particularly effective when the number of documents (10,000) is moderate relative to the vocabulary size, as it requires relatively little training data to estimate parameters.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Principal Component Analysis (PCA)
Why it's wrong here
PCA is for dimensionality reduction, not classification.
✓
Naive Bayes
Why this is correct
Naive Bayes is effective for text classification and small datasets.
✗
Linear regression
Why it's wrong here
Linear regression is for regression, not classification.
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k-means clustering
Why it's wrong here
k-means is unsupervised and not for classification.

Go deeper

MLS-C01 Modeling Practice Question

Why each option matters

Option-by-option breakdown

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