👉 Evaluations math, often used in the context of machine learning and deep learning, refers to a set of techniques that assess the performance and effectiveness of models by comparing their predictions against known outcomes. This process typically involves calculating metrics such as accuracy, precision, recall, F1 score, mean squared error (MSE), or area under the ROC curve (AUC-ROC), depending on the task (classification, regression, etc.). These metrics provide insights into how well a model generalizes to new data and identifies areas for improvement, such as adjusting hyperparameters or refining the model architecture. By systematically evaluating models through these mathematical assessments, practitioners can make data-driven decisions to enhance model performance and reliability.
