Model Performance

Model performance refers to the ability of a machine learning model to accurately predict outcomes on new, unseen data. It is a measure of how well the model has learned the underlying patterns and relationships in the training data. Model performance is typically evaluated using metrics such as accuracy, precision, recall, F1 score, and area under the curve (AUC). These metrics provide a quantitative measure of the model's ability to correctly classify or predict outcomes. Model performance can be improved through techniques such as hyperparameter tuning, feature selection, and ensemble methods.
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