R로 배우는 신용 위험 모델링
Lore Dirick
Manager of Data Science Curriculum at Flatiron School
$$
| 부도 없음 (0) | 부도 (1) | |
|---|---|---|
| 부도 없음 (0) | TN | FP |
| 부도 (1) | FN | TP |
$$
$\text{Accuracy} = \frac{TP +TN}{TP + FP + TN + FN}$
$\text{Sensitivity} = \frac{TP}{TP + FN}$
$\text{Specificity} = \frac{TN}{TN + FP}$
$$

$$
$\text{Sensitivity} = \frac{TP}{TP + FN}$
$\text{Specificity} = \frac{TN}{TN + FP}$

$$
$\text{Sensitivity} = \frac{TP}{TP + FN}$
$\text{Specificity} = \frac{TN}{TN + FP}$

$$
$\text{Sensitivity} = \frac{TP}{TP + FN}$
$\text{Specificity} = \frac{TN}{TN + FP}$

$$
$\text{Sensitivity} = \frac{TP}{TP + FN}$
$\text{Specificity} = \frac{TN}{TN + FP}$

$$
$\text{Sensitivity} = \frac{TP}{TP + FN}$
$\text{Specificity} = \frac{TN}{TN + FP}$

$$
$\text{Sensitivity} = \frac{TP}{TP + FN}$
$\text{Specificity} = \frac{TN}{TN + FP}$

$$
$\text{Sensitivity} = \frac{TP}{TP + FN}$
$\text{Specificity} = \frac{TN}{TN + FP}$

A = 0.75B = 0.78R로 배우는 신용 위험 모델링