👉 Evaluation math in machine learning refers to the process of assessing the performance and accuracy of a model using mathematical functions and statistical measures. It involves calculating metrics such as accuracy, precision, recall, F1 score, mean squared error (MSE), and R-squared to quantify how well the model predicts or classifies data. These metrics are derived from loss functions, which measure the difference between the model's predictions and the actual outcomes. By analyzing these mathematical evaluations, data scientists can fine-tune their models, identify areas for improvement, and make informed decisions about model selection or hyperparameter tuning to achieve optimal performance.
