Model metrics

Model metrics refer to quantitative measures used to evaluate the performance and effectiveness of machine learning models. These metrics assess various aspects such as accuracy, precision, recall, F1-score, mean squared error (MSE), and others, providing insights into how well the model performs its intended task and informing decisions on model selection and refinement.