标签：TN frac Python Recall Precision TP Machine beta Learning
| Metric |
Formula |
Interpretation |
| Accuracy |
$ \frac{TP+TN}{TP+TN+FP+FN} $ |
Overall performance of model |
| Precision |
$ \frac{TP}{TP+FN} $ |
How accurate the positive predictions are |
| Recall Sensitivity |
$ \frac{TP}{TP+FP} $ |
Coverage of actual positive sample |
| Specificity |
$ \frac{TN}{TN+FN} $ |
Coverage of actual negative sample |
| F-1 score |
$ \frac{2(Precision * Recall)}{2* Precision + Recall} $ |
Hybrid metric useful for unbalanced classes |
| F-beta score |
$ \frac{(1+\beta^2)(Precision * Recall)}{\beta^2 * Prediction + Recall} $ |
F-1 score generalized form (\beta = 1) |
标签：TN,
frac,
Python,
Recall,
Precision,
TP,
Machine,
beta,
Learning
From： https://www.cnblogs.com/forhheart/p/17910386.html