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Résumé : Machine learning is used in many fields because of its rapid development. To determine their efficacy and suitability for certain activities, these models must be evaluated. This study maps the knowledge domain of machine learning approaches to evaluate performance, revealing its important components. Performance metrics-quantitative measurements of machine learning model performance are examined first. Predictive and generalization metrics include accuracy, precision, recall, F1-score, and area under the ROC curve. Performance evaluation requires understanding these indicators' calculation and interpretation. Cross-validation, holdout validation, k-fold cross-validation, and bootstrapping are examined next. These methods reveal the model's performance on unseen data and reduce overfitting and underfitting. This study also addresses hyperparameter tweaking. Optimizing machine learning model hyperparameters including learning rate, regularization parameters, and network design is crucial to performance. Find the optimal hyperparameter configuration using grid search, random search, and Bayesian optimization. Finally, model selection criteria determine the best model for a task. Performance measurements, complexity, and computational resources help make decisions. This study maps the knowledge domain of using machine learning techniques to performance evaluation, revealing the fundamental features. This knowledge helps researchers and practitioners evaluate machine learning models and make educated real-world judgements.