Answer: (iv) Naive Bayes — generally considered best baseline for text classification, though context dependent.

Explanation:

• Naive Bayes (Multinomial/Bernoulli): Highly effective for high-dimensional sparse text (Bag-of-Words / TF-IDF). Fast, works well with small data, handles thousands of features, empirically strong for spam, sentiment, topic classification. Assumption of conditional independence of words given class surprisingly works well in practice.
• KNN: Lazy, computationally expensive at inference, suffers from curse of dimensionality in text.
• Decision Tree: Prone to overfit on sparse text, not ideal.
• Random Forest: Better than single tree but slower and often still outperformed by NB/SVM for raw text without embeddings.

In modern deep learning era, Transformers/BERT outperform all, but among given classical options, Naive Bayes (and Linear SVM — not listed) is textbook best for text. Some keys may accept Random Forest as competitive, but traditional exam answer is (iv) Naive Bayes.