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Which Steps Reproduce Test Configurations in Machine Learning Evaluations Accurately?
Learn essential actions for documenting sampling strategies and test configurations in ML to guarantee reproducibility, including hardware specs, data processes, and parameters for reliable model evaluations.
Question
Which of the following actions are essential when documenting sampling strategies and test configurations to ensure reproducibility of model evaluation?
A. Listing the software and hardware specifications
B. Stating the business objectives
C. Detailing the data collection process
D. Recording all parameter settings
E. Describing the algorithms used
Answer
A. Listing the software and hardware specifications
C. Detailing the data collection process
D. Recording all parameter settings
Explanation
Documenting sampling strategies and test configurations for reproducible model evaluation requires recording specifics that allow exact replication of the data splits, preprocessing, and evaluation pipeline, making A (listing software/hardware specs) essential to match computational environments that influence random seeds and numerical precision; C (detailing data collection) critical for recreating identical input distributions and splits; and D (recording all parameter settings) vital for identical model configurations and hyperparameters, as even minor variations can alter outcomes significantly.