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Table 2 Performance metrics for accuracy validation

From: A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

Metric Description
Mean Absolute Error (MAE) Average of the absolute error between the actual and predicted values. Facilitates error interpretability.
Mean Squared Error (MSE) Average of the squares of the error between the actual and predicted values. Heavily penalizes large errors.
Mean Absolute Prediction Error (MAPE) Percentage of the error between the actual and predicted values. Not reliable for zero values or low-scale data.
Root MSE (RMSE) Squared root of MSE. Represents the standard deviation of the error between the actual and predicted values.
Normalized RMSE (NRMSE) Normalized RMSE. Facilitates comparing different models independently of their working scale.
Cross-entropy Metric based on the logistic function that measures the error between the actual and predicted values.
Accuracy Proportion of correct predictions among the total number of predictions. Not reliable for skewed class-wise data.
True Positive Rate (TPR) or recall Proportion of actual positives that are correctly predicted. Represents the sensitivity or detection rate (DR) of a model.
False Positive Rate (FPR) Proportion of actual negatives predicted as positives. Represents the significance level of a model.
True Negative Rate (TNR) Proportion of actual negatives that are correctly predicted. Represents the specificity of a model.
False Negative Rate (FNR) Proportion of actual positives predicted as negatives. Inversely proportional to the statistical power of a model.
Received Operating Characteristic (ROC) Curve that plots TPR versus FPR at different parameter settings. Facilitates analyzing the cost-benefit of possibly optimal models.
Area Under the ROC Curve (AUC) Probability of confidence in a model to accurately predict positive outcomes for actual positive instances.
Precision Proportion of positive predictions that are correctly predicted.
F-measure Harmonic mean of precision and recall. Facilitates analyzing the trade-off between these metrics.
Coefficient of Variation (CV) Intra-cluster similarity to measure the accuracy of unsupervised classification models based on clusters.