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. |