From: NeoMutate: an ensemble machine learning framework for the prediction of somatic mutations in cancer
Metric | Formula | Definition |
---|---|---|
Accuracy | \( \frac{TP+ TN}{\left( TP+ TN+ FP+ FN\right)} \) | The ratio of correct calls out of the total number of positions. |
Precision | \( \frac{TP}{\left( TP+ FP\right)} \) | The ratio of correct variant calls out of the total number of variant calls. Synonyms: Positive predictive value (PPV) |
Recall | \( \frac{TP}{\left( TP+ FN\right)} \) | The ratio of correct variant calls out of the total number of variant positions. Synonyms: Sensitivity, true-positive rate (TPR). |
False discovery rate (FDR) | \( \frac{FP}{\left( TP+ FP\right)} \) | The ratio of incorrect calls out of the total number of variant calls. |
F1-Score | \( \frac{2\ast Precision\ast Recall}{\left( Precision+ Recall\right)} \) | Harmonic mean of precision and recall, where 1 is the best score and 0 the worst. Synonyms: F-score |
Matthews correlation coefficient (MCC) | \( \frac{TP\ast TN- FP\ast FN}{\sqrt{\left( TP+ FP\left)\right( TP+ FN\right)\left( TN+ FP\right)\left( TN+ FN\right)}} \) | A measure of the quality of binary (two-class) classifications. The MCC represents the correlation coefficient between the observed and predicted binary classifications, where −1 indicates a completely wrong binary classifier while 1 indicates a completely correct classifier. |