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 lowscale 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.

Crossentropy

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 classwise 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 costbenefit 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.

Fmeasure

Harmonic mean of precision and recall. Facilitates analyzing the tradeoff between these metrics.

Coefficient of Variation (CV)

Intracluster similarity to measure the accuracy of unsupervised classification models based on clusters.
