trainingsettings
Training settings, or training configurations, refer to the set of parameters that govern how a machine learning model is trained. They specify how data is prepared, how the model is updated, and how training progress is measured. In practice, training settings are captured in configuration files or objects used by ML frameworks to initialize the training run, ensuring consistency across experiments.
Key components typically include data handling (batch size, shuffling, train/validation/test split; data augmentation), model specification (architecture,
Impact and best practices: Training settings influence convergence speed, stability, generalization, and resource usage. They require