Data splitting partitions a labeled dataset into training, validation, and test subsets to enable unbiased model tuning and evaluation. Training (A) consumes the training split; annotating (B) adds labels; learning (D) is a general term for model optimization, not a data management step.
[References: AI Security Management™ (AAISM) Body of Knowledge — Data Lifecycle Controls; Dataset Partitioning for Validation and Testing. AAISM Study Guide — Train/Validation/Test Splits and Evaluation Integrity., ===========]
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