Description: The Evaluation Protocol in machine learning refers to a set of guidelines and procedures designed to measure the performance of a machine learning model. This protocol is essential to ensure that models are not only accurate but also robust and generalizable to unseen data. It includes specific metrics that allow quantifying the model’s effectiveness, such as accuracy, precision, recall, F1 score, and area under the ROC curve, among others. Additionally, the protocol establishes methods for splitting data into training, validation, and test sets, which helps prevent overfitting and evaluate performance under controlled conditions. Implementing an appropriate Evaluation Protocol is crucial for interpreting results and making informed decisions about deploying the model in real-world applications. In summary, this protocol not only provides a framework for evaluation but also promotes transparency and reproducibility in the development of machine learning models.
