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Mean absolute error (MAE) is a commonly used accuracy metric in prediction tasks, computed as the average of the absolute errors between predicted and true values, in milliseconds (ms).
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10/26/2026
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UnverifiedMAPE is a commonly used accuracy metric in regression tasks, calculated as the absolute value of the difference between the predicted value and the true value divided by the true value, then averaged over all samples and multiplied by 100%; its limitation is that when the true value is close to zero it gets amplified or even undefined, so it is more applicable in industrial scenarios such as flow rate and production volume where values are always positive and of stable magnitude.77% similarUnverifiedMASE (Mean Absolute Scaled Error) is a dimensionless error metric commonly used in time series forecasting, dividing the absolute error by the mean absolute error of a naive baseline on the training set.70% similarUnverifiedThe error exponent is the core metric for measuring the performance of best-arm identification algorithms: under the large deviation theory framework, as the total budget tends to infinity, the algorithm's probability of error decays at an exponential rate, and that decay rate is the error exponent.66% similarUnverifiedMPE is defined as the expected optimal conditional prediction accuracy minus the optimal static prediction accuracy.66% similarUnverified回归任务中的均方误差（MSE）对应的是在高斯噪声假设下的最大似然估计66% similar
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