ML Metrics
ML Metrics
FreeThe 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)
