Performance Metrics In Machine Learning

Performance Metrics In Machine Learning

Performance metrics in Machine Learning are quantitative measures used to evaluate the effectiveness, accuracy, and reliability of predictive models. These metrics assess how well a model generalizes to unseen data and guide model selection, tuning, and optimization. Depending on the task—classification, regression, or clustering—different evaluation criteria such as accuracy, precision, recall, F1-score, ROC-AUC, mean squared error (MSE), and R-squared are applied. Proper selection of performance metrics ensures fair comparison between models, prevents overfitting, and supports robust, data-driven deployment in domains such as healthcare, finance, cybersecurity, and scientific research.

Machine Learning Evaluation, Performance Metrics, Model Evaluation, Accuracy, Precision, Recall, F1-Score, ROC Curve, AUC, Confusion Matrix, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R-Squared, Cross-Validation, Overfitting, Bias-Variance Tradeoff, Model Validation, Predictive Modeling, Classification Metrics, Regression Metrics.

#MachineLearning #ModelEvaluation #PerformanceMetrics #Accuracy #PrecisionRecall #F1Score #ROCAUC #MSE #RMSE #CrossValidation #PredictiveModeling #DataScience #AI #ModelOptimization #Analytics

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