【机器学习】（一）模型性能评估

机器学习中常用的模型性能评估指标，来源于维基百科

sensitivity, recall, hit rate, or true positive rate (TPR): $\mathrm {TPR} ={\frac {\mathrm {TP} }{P}}={\frac {\mathrm {TP} }{\mathrm {TP} +\mathrm {FN} }}$
specificity or true negative rate (TNR): $\mathrm {TNR} ={\frac {\mathrm {TN} }{N}}={\frac {\mathrm {TN} }{\mathrm {TN} +\mathrm {FP} }}$
precision or positive predictive value (PPV): $\mathrm {PPV} ={\frac {\mathrm {TP} }{\mathrm {TP} +\mathrm {FP} }}$
negative predictive value (NPV): $\mathrm {NPV} ={\frac {\mathrm {TN} }{\mathrm {TN} +\mathrm {FN} }}$
miss rate or false negative rate (FNR): $\mathrm {FNR} ={\frac {\mathrm {FN} }{P}}={\frac {\mathrm {FN} }{\mathrm {FN} +\mathrm {TP} }}=1-\mathrm {TPR}$
fall-out or false positive rate (FPR): $\mathrm {FPR} ={\frac {\mathrm {FP} }{N}}={\frac {\mathrm {FP} }{\mathrm {FP} +\mathrm {TN} }}=1-\mathrm {TNR}$
false discovery rate (FDR): $\mathrm {FDR} ={\frac {\mathrm {FP} }{\mathrm {FP} +\mathrm {TP} }}=1-\mathrm {PPV}$
false omission rate (FOR): $\mathrm {FOR} ={\frac {\mathrm {FN} }{\mathrm {FN} +\mathrm {TN} }}=1-\mathrm {NPV}$
accuracy (ACC): $\mathrm {ACC} ={\frac {\mathrm {TP} +\mathrm {TN} }{P+N}}={\frac {\mathrm {TP} +\mathrm {TN} }{\mathrm {TP} +\mathrm {TN} +\mathrm {FP} +\mathrm {FN} }}$

F1 score: is the harmonic mean of precision and sensitivity; $F_{1}=2\cdot {\frac {\mathrm {PPV} \cdot \mathrm {TPR} }{\mathrm {PPV} +\mathrm {TPR} }}={\frac {2\mathrm {TP} }{2\mathrm {TP} +\mathrm {FP} +\mathrm {FN} }}$
Matthews correlation coefficient (MCC): $\mathrm {MCC} ={\frac {\mathrm {TP} \times \mathrm {TN} -\mathrm {FP} \times \mathrm {FN} }{\sqrt {(\mathrm {TP} +\mathrm {FP} )(\mathrm {TP} +\mathrm {FN} )(\mathrm {TN} +\mathrm {FP} )(\mathrm {TN} +\mathrm {FN} )}}}$
Informedness or Bookmaker Informedness (BM): $\mathrm {BM} =\mathrm {TPR} +\mathrm {TNR} -1$
Markedness (MK): $\mathrm {MK} =\mathrm {PPV} +\mathrm {NPV} -1$

混淆矩阵的表示方法如下图：