Machine Learning Testing Metrics
Machine Learning Testing Metrics refer to various quantitative measures used to evaluate the performance and effectiveness of machine learning models. These metrics help assess the accuracy, reliability, and efficiency of algorithms in processing and analyzing data. Common metrics include precision, recall, F1 score, accuracy, ROC-AUC, and confusion matrix. These metrics provide insights into the model's ability to make correct predictions, identify patterns, and generalize to unseen data. Overall, Machine Learning Testing Metrics play a crucial role in assessing the quality and robustness of machine learning models.