<Dt> accuracy (ACC) </Dt> <Dd> A C C = T P + T N P + N = T P + T N T P + T N + F P + F N (\ displaystyle \ mathrm (ACC) = (\ frac (\ mathrm (TP) + \ mathrm (TN)) (P + N)) = (\ frac (\ mathrm (TP) + \ mathrm (TN)) (\ mathrm (TP) + \ mathrm (TN) + \ mathrm (FP) + \ mathrm (FN)))) </Dd> <Dl> <Dt> F1 score </Dt> <Dd> is the harmonic mean of precision and sensitivity </Dd> <Dd> F 1 = 2 ⋅ P P V ⋅ T P R P P V + T P R = 2 T P 2 T P + F P + F N (\ displaystyle F_ (1) = 2 \ cdot (\ frac (\ mathrm (PPV) \ cdot \ mathrm (TPR)) (\ mathrm (PPV) + \ mathrm (TPR))) = (\ frac (2 \ mathrm (TP)) (2 \ mathrm (TP) + \ mathrm (FP) + \ mathrm (FN)))) </Dd> <Dt> Matthews correlation coefficient (MCC) </Dt> <Dd> M C C = T P × T N − F P × F N (T P + F P) (T P + F N) (T N + F P) (T N + F N) (\ displaystyle \ mathrm (MCC) = (\ frac (\ mathrm (TP) \ times \ mathrm (TN) - \ mathrm (FP) \ times \ mathrm (FN)) (\ sqrt ((\ mathrm (TP) + \ mathrm (FP)) (\ mathrm (TP) + \ mathrm (FN)) (\ mathrm (TN) + \ mathrm (FP)) (\ mathrm (TN) + \ mathrm (FN)))))) </Dd> <Dt> Informedness or Bookmaker Informedness (BM) </Dt> <Dd> B M = T P R + T N R − 1 (\ displaystyle \ mathrm (BM) = \ mathrm (TPR) + \ mathrm (TNR) - 1) </Dd> <Dt> Markedness (MK) </Dt> <Dd> M K = P P V + N P V − 1 (\ displaystyle \ mathrm (MK) = \ mathrm (PPV) + \ mathrm (NPV) - 1) </Dd> </Dl> <Dd> is the harmonic mean of precision and sensitivity </Dd>

True positive and false positive in machine learning