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What is the primary purpose of hyperparameter tuning in machine learning (ML)?

publish date: 2025/09/15 04:25:18.250094 UTC

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To create new training data

To adjust model parameters for improved performance

To convert categorical data into numerical form

To speed up the training process

Correct Answer

To adjust model parameters for improved performance

Explanation

Hyperparameter tuning optimizes model performance by adjusting parameters such as the learning rate and batch size. it does not generate new data. Data preprocessing, not hyperparameter tuning, converts categorical data. Tuning may increase training time rather than speed it up.

Reference

AWS Certified AI Practitioner (AIF-C01) Study Guide, Tom Taulli

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