👉 Nomination computing, also known as nomination-phase computing or candidate selection, is a crucial phase in machine learning model training, particularly in supervised learning tasks like classification and regression. It involves selecting the most appropriate model from a set of candidate models based on their performance on a validation set. During this phase, each candidate model's predictions are compared against the true labels to calculate a performance metric, such as accuracy or F1 score. The models with the highest scores are then chosen for final evaluation and deployment. This process helps in identifying the model that best generalizes to unseen data, ensuring optimal performance and reducing overfitting.
