Classification error metrics are methods for evaluating the performance of a classifier, which is a type of machine learning model that is trained to assign a class label to an input example. Some common classification error metrics include accuracy, precision, recall, and F1 score. Classification errors are mistakes made by a classification algorithm in predicting the correct class or category for a given data point. Classification errors can occur when the algorithm is trained on a limited or noisy dataset, or when the data is complex or non-linear, and cannot be accurately modeled by the algorithm. Classification errors can have a range of negative consequences, depending on the application, including incorrect medical diagnoses, fraudulent transactions, or poor customer experiences.
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