What is model evaluation?

Model evaluation is the process of assessing the performance of a machine learning model on a given dataset. It involves using various metrics and techniques to quantify how well the model is generalizing to unseen data and identifying potential areas for improvement. Common methods include using holdout sets, cross-validation, and calculating metrics such as accuracy, precision, recall, F1-score, and AUC-ROC.