MLS-C01 Question #154: Real Exam Question with Answer & Explanation

Question

A company has a 1,000 sentences with sentiments categorized as positive, neutral, or negative. Which modeling technique should a Machine Learing Specialist select for training a baseline sentiment model?

Options

• AK-means
• BMultinomial logistic regression
• CRecurrent neura network (RNN)
• DTransfer learning

Explanation

For training a baseline sentiment model on 1,000 labeled sentences categorized as positive, neutral, or negative, a multinomial logistic regression is a suitable choice due to its simplicity, efficiency, and effectiveness for multi-class classification tasks.

Common mistakes.

• A. K-means is an unsupervised clustering algorithm, whereas the problem is a supervised classification task with labeled sentiment categories.
• C. Recurrent Neural Networks (RNNs) are powerful deep learning models for sequence data, but they are generally more complex, computationally intensive, and require more data to train effectively than what is typically considered a baseline model, especially with only 1,000 sentences.
• D. Transfer learning, while highly effective for natural language processing, involves fine-tuning large pre-trained models and is typically a more advanced approach rather than a simple baseline, incurring higher computational and resource overhead.

Concept tested. Baseline model selection for multi-class text classification

Reference. https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html

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