Question 20
Domain 2 — Data, Machine Learning, and Model DevelopmentIn which phase of the machine learning process are hyperparameters tuned to optimize model performance?
Correct answer: E
Explanation
Hyperparameters are adjusted during the hyperparameter tuning phase to improve model performance. This step involves selecting values such as learning rate, depth, or regularization before finalizing the model, which is why the phase is called "Hyperparameter Tuning."
Why each option is right or wrong
A. Model Deployment
B. Model Evaluation
C. Model Training
D. Feature Engineering
E. Hyperparameter Tuning
Hyperparameters are selected and adjusted in the dedicated model-selection step, after the training procedure has been defined but before the final model is fixed. In standard machine-learning workflows, this phase is explicitly called hyperparameter tuning because it searches over values such as learning rate, tree depth, or regularization strength to maximize validation performance rather than fitting the model weights themselves.
F. Data Preprocessing