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ML Metrics
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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)
Downloads: 447+ (for Android)
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