Performance Metrics

Performance metrics are quantitative measures used to assess the effectiveness and efficiency of a system, process, or algorithm. In the context of data science and artificial intelligence, performance metrics are used to evaluate the performance of machine learning models. These metrics are used to determine how well a model is performing in terms of accuracy, precision, recall, F1 score, and other measures. Performance metrics are essential for model selection, hyperparameter tuning, and model optimization. They are also used to compare different models and to identify the best model for a given task. Performance metrics can be divided into two categories: classification metrics and regression metrics. Classification metrics are used for classification problems, while regression metrics are used for regression problems. Some common performance metrics used in data science and artificial intelligence include accuracy, precision, recall, F1 score, ROC curve, AUC, and mean squared error.
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