Errate

Errate, or error rate, is a measure used to quantify how often a system, process, or dataset yields incorrect results. It is typically defined as the proportion of errors among total trials, E/N, and is often expressed as a percentage. In some fields, the term error rate is used more specifically to denote particular kinds of errors, such as bit error rate (BER) in communications or character/word error rate in text recognition tasks.

Calculation and interpretation of errate depend on the context. In machine learning and data classification, the error rate is the fraction of incorrect predictions, while accuracy is its complement. A lower error rate indicates better performance, with 0% representing perfect results on the tested data. Confidence intervals may be reported to reflect sampling variability, especially for small or uneven datasets.

Applications span many domains. In communications, BER or SER describe the likelihood of incorrect bit or symbol reception. In pattern recognition, OCR and speech or natural language processing, error rate measures misclassification or transcription mistakes. In quality control and software testing, error rate can track defect occurrence or failure rates over time.

Limitations and nuances exist. Error rate depends on the test set and may be misleading on imbalanced data or when different errors have unequal consequences. Therefore, it is common to supplement errate with other metrics such as precision, recall, F1 score, or area under the ROC curve, depending on the task and goals. See also accuracy, confusion matrix, and related evaluation metrics.