ML Metrics

The app shows the performance of system depend upon the following metrices

Accuracy

Precision Error Rate Recall TPR FNR FDR FPR Sensitivity Specificity

This app also show ROC (Receiver Operating Characteristic) Curve with AUC (Area Under Curve) Score for better representation.

Recall: Out of all the positive classes, how many instances were identified correctly.

Precision or Positive Predictive Value (PPV): Out of all the predicted positive instances, how many were predicted correctly.

F-Score: From Precision and Recall, F-Measure is computed and used as metrics sometimes. F – Measure is nothing but the harmonic mean of Precision and Recall.

Sensitivity or True Positive Rate (TPR)

Specificity (SPC) or True Negative Rate (TNR) or 1-FPR

Negative Predictive Value (NPV)

Fall-out or False Positive Rate (FPR)

False Discovery Rate (FDR):

Miss Rate or False Negative Rate (FNR)

Accuracy (ACC)