Code
rbin_manual(mbank, y, age, c(29, 31, 34, 36, 39, 42, 46, 51, 56))
Output
Binning Summary
---------------------------
Method Manual
Response y
Predictor age
Bins 10
Count 4521
Goods 517
Bads 4004
Entropy 0.5
Information Value 0.12
cut_point bin_count good bad woe iv entropy
1 < 29 410 71 339 -0.483686036 2.547353e-02 0.6649069
2 < 31 313 41 272 -0.154776266 1.760055e-03 0.5601482
3 < 34 567 55 512 0.183985174 3.953685e-03 0.4594187
4 < 36 396 45 351 0.007117468 4.425063e-06 0.5107878
5 < 39 519 47 472 0.259825118 7.008270e-03 0.4383322
6 < 42 431 33 398 0.442938178 1.575567e-02 0.3899626
7 < 46 449 47 402 0.099298221 9.423907e-04 0.4836486
8 < 51 521 40 481 0.439981550 1.881380e-02 0.3907140
9 < 56 445 49 396 0.042587647 1.756117e-04 0.5002548
10 >= 56 470 89 381 -0.592843261 4.564428e-02 0.7001343
Code
rbin_factor(mbank, y, education)
Output
Binning Summary
---------------------------
Method Custom
Response y
Predictor education
Levels 4
Count 4521
Goods 517
Bads 4004
Entropy 0.51
Information Value 0.05
level bin_count good bad woe iv entropy
1 tertiary 1299 195 1104 -0.3133106 0.031785905 0.6101292
2 secondary 2352 231 2121 0.1702190 0.014113157 0.4633093
3 primary 691 66 625 0.2010906 0.005717878 0.4546110
4 unknown 179 25 154 -0.2289295 0.002265111 0.5833603
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