Skill

ML Model Evaluation

Data Science, Analytics and AI/ML

ML model evaluation is the practice of measuring how well a trained machine learning model performs on unseen data, using metrics like accuracy, precision, recall, F1 score, or AUC depending on the task. Data scientists rely on techniques such as train/test splits, cross-validation, and holdout sets to detect overfitting and compare candidate models before deployment. It is a core step in the ML lifecycle that determines whether a model is ready for production use.

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• Hyperparameter Tuning