Toward an Efficient and Effective Credit Scorer for Cross-Border E-Commerce Enterprises

Table 1

WOE for the “average age of the customers.” The values are binned using equal-frequency clustering and then normalized.

Bin	WOE
1: [0, 0.0028]	−1.4633
2: [0.0028, 0.0061]	−0.639
3: [0.0061, 0.0082]	−0.4933
4: [0.0082, 0.0149]	0.0276
5: [0.0149, 0.0215]	0.5489
6: [0.0215, 0.048]	0.5802
7: [0.048, 0.0607]	0.8021
8: [0.0607, 1]	0.9527