Datasets:
feature_1
bool 2
classes | feature_2
float64 13.8
80.3
| feature_3
float64 0
28
| feature_4
stringclasses 3
values | feature_5
stringclasses 14
values | feature_6
stringclasses 8
values | feature_7
float64 0
28.5
| feature_8
stringclasses 2
values | feature_9
stringclasses 2
values | feature_10
float64 0
67
| feature_11
stringclasses 2
values | feature_12
stringclasses 3
values | feature_13
float64 0
2k
| feature_14
float64 1
100k
| is_granted
class label 2
classes |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
true | 22.08 | 11.46 | 2.0 | 4.0 | 4.0 | 1.585 | 0.0 | 0.0 | 0 | 1.0 | 2.0 | 100 | 1,213 | 0no
|
false | 22.67 | 7 | 2.0 | 8.0 | 4.0 | 0.165 | 0.0 | 0.0 | 0 | 0.0 | 2.0 | 160 | 1 | 0no
|
false | 29.58 | 1.75 | 1.0 | 4.0 | 4.0 | 1.25 | 0.0 | 0.0 | 0 | 1.0 | 2.0 | 280 | 1 | 0no
|
false | 21.67 | 11.5 | 1.0 | 5.0 | 3.0 | 0 | 1.0 | 1.0 | 11 | 1.0 | 2.0 | 0 | 1 | 1yes
|
true | 20.17 | 8.17 | 2.0 | 6.0 | 4.0 | 1.96 | 1.0 | 1.0 | 14 | 0.0 | 2.0 | 60 | 159 | 1yes
|
false | 15.83 | 0.585 | 2.0 | 8.0 | 8.0 | 1.5 | 1.0 | 1.0 | 2 | 0.0 | 2.0 | 100 | 1 | 1yes
|
true | 17.42 | 6.5 | 2.0 | 3.0 | 4.0 | 0.125 | 0.0 | 0.0 | 0 | 0.0 | 2.0 | 60 | 101 | 0no
|
false | 58.67 | 4.46 | 2.0 | 11.0 | 8.0 | 3.04 | 1.0 | 1.0 | 6 | 0.0 | 2.0 | 43 | 561 | 1yes
|
true | 27.83 | 1 | 1.0 | 2.0 | 8.0 | 3 | 0.0 | 0.0 | 0 | 0.0 | 2.0 | 176 | 538 | 0no
|
false | 55.75 | 7.08 | 2.0 | 4.0 | 8.0 | 6.75 | 1.0 | 1.0 | 3 | 1.0 | 2.0 | 100 | 51 | 0no
|
true | 33.5 | 1.75 | 2.0 | 14.0 | 8.0 | 4.5 | 1.0 | 1.0 | 4 | 1.0 | 2.0 | 253 | 858 | 1yes
|
true | 41.42 | 5 | 2.0 | 11.0 | 8.0 | 5 | 1.0 | 1.0 | 6 | 1.0 | 2.0 | 470 | 1 | 1yes
|
true | 20.67 | 1.25 | 1.0 | 8.0 | 8.0 | 1.375 | 1.0 | 1.0 | 3 | 1.0 | 2.0 | 140 | 211 | 0no
|
true | 34.92 | 5 | 2.0 | 14.0 | 8.0 | 7.5 | 1.0 | 1.0 | 6 | 1.0 | 2.0 | 0 | 1,001 | 1yes
|
true | 58.58 | 2.71 | 2.0 | 8.0 | 4.0 | 2.415 | 0.0 | 0.0 | 0 | 1.0 | 2.0 | 320 | 1 | 0no
|
true | 48.08 | 6.04 | 2.0 | 4.0 | 4.0 | 0.04 | 0.0 | 0.0 | 0 | 0.0 | 2.0 | 0 | 2,691 | 1yes
|
true | 29.58 | 4.5 | 2.0 | 9.0 | 4.0 | 7.5 | 1.0 | 1.0 | 2 | 1.0 | 2.0 | 330 | 1 | 1yes
|
false | 18.92 | 9 | 2.0 | 6.0 | 4.0 | 0.75 | 1.0 | 1.0 | 2 | 0.0 | 2.0 | 88 | 592 | 1yes
|
true | 20 | 1.25 | 1.0 | 4.0 | 4.0 | 0.125 | 0.0 | 0.0 | 0 | 0.0 | 2.0 | 140 | 5 | 0no
|
false | 22.42 | 5.665 | 2.0 | 11.0 | 4.0 | 2.585 | 1.0 | 1.0 | 7 | 0.0 | 2.0 | 129 | 3,258 | 1yes
|
false | 28.17 | 0.585 | 2.0 | 6.0 | 4.0 | 0.04 | 0.0 | 0.0 | 0 | 0.0 | 2.0 | 260 | 1,005 | 0no
|
false | 19.17 | 0.585 | 1.0 | 6.0 | 4.0 | 0.585 | 1.0 | 0.0 | 0 | 1.0 | 2.0 | 160 | 1 | 0no
|
true | 41.17 | 1.335 | 2.0 | 2.0 | 4.0 | 0.165 | 0.0 | 0.0 | 0 | 0.0 | 2.0 | 168 | 1 | 0no
|
true | 41.58 | 1.75 | 2.0 | 4.0 | 4.0 | 0.21 | 1.0 | 0.0 | 0 | 0.0 | 2.0 | 160 | 1 | 0no
|
true | 19.5 | 9.585 | 2.0 | 6.0 | 4.0 | 0.79 | 0.0 | 0.0 | 0 | 0.0 | 2.0 | 80 | 351 | 0no
|
true | 32.75 | 1.5 | 2.0 | 13.0 | 8.0 | 5.5 | 1.0 | 1.0 | 3 | 1.0 | 2.0 | 0 | 1 | 1yes
|
true | 22.5 | 0.125 | 1.0 | 4.0 | 4.0 | 0.125 | 0.0 | 0.0 | 0 | 0.0 | 2.0 | 200 | 71 | 0no
|
true | 33.17 | 3.04 | 1.0 | 8.0 | 8.0 | 2.04 | 1.0 | 1.0 | 1 | 1.0 | 2.0 | 180 | 18,028 | 1yes
|
false | 30.67 | 12 | 2.0 | 8.0 | 4.0 | 2 | 1.0 | 1.0 | 1 | 0.0 | 2.0 | 220 | 20 | 1yes
|
true | 23.08 | 2.5 | 2.0 | 8.0 | 4.0 | 1.085 | 1.0 | 1.0 | 11 | 1.0 | 2.0 | 60 | 2,185 | 1yes
|
true | 27 | 0.75 | 2.0 | 8.0 | 8.0 | 4.25 | 1.0 | 1.0 | 3 | 1.0 | 2.0 | 312 | 151 | 1yes
|
false | 20.42 | 10.5 | 1.0 | 14.0 | 8.0 | 0 | 0.0 | 0.0 | 0 | 1.0 | 2.0 | 154 | 33 | 0no
|
true | 52.33 | 1.375 | 1.0 | 8.0 | 8.0 | 9.46 | 1.0 | 0.0 | 0 | 1.0 | 2.0 | 200 | 101 | 0no
|
true | 23.08 | 11.5 | 2.0 | 9.0 | 8.0 | 2.125 | 1.0 | 1.0 | 11 | 1.0 | 2.0 | 290 | 285 | 1yes
|
true | 42.83 | 1.25 | 2.0 | 7.0 | 4.0 | 13.875 | 0.0 | 1.0 | 1 | 1.0 | 2.0 | 352 | 113 | 0no
|
true | 74.83 | 19 | 1.0 | 1.0 | 1.0 | 0.04 | 0.0 | 1.0 | 2 | 0.0 | 2.0 | 0 | 352 | 0no
|
true | 25 | 12.5 | 2.0 | 6.0 | 4.0 | 3 | 1.0 | 0.0 | 0 | 1.0 | 1.0 | 20 | 1 | 1yes
|
true | 39.58 | 13.915 | 2.0 | 9.0 | 4.0 | 8.625 | 1.0 | 1.0 | 6 | 1.0 | 2.0 | 70 | 1 | 1yes
|
false | 47.75 | 8 | 2.0 | 8.0 | 4.0 | 7.875 | 1.0 | 1.0 | 6 | 1.0 | 2.0 | 0 | 1,261 | 1yes
|
false | 47.42 | 3 | 2.0 | 14.0 | 4.0 | 13.875 | 1.0 | 1.0 | 2 | 1.0 | 2.0 | 519 | 1,705 | 1yes
|
true | 23.17 | 0 | 2.0 | 13.0 | 4.0 | 0.085 | 1.0 | 0.0 | 0 | 0.0 | 2.0 | 0 | 1 | 1yes
|
true | 22.58 | 1.5 | 1.0 | 6.0 | 4.0 | 0.54 | 0.0 | 0.0 | 0 | 1.0 | 2.0 | 120 | 68 | 0no
|
true | 26.75 | 1.125 | 2.0 | 14.0 | 8.0 | 1.25 | 1.0 | 0.0 | 0 | 0.0 | 2.0 | 0 | 5,299 | 1yes
|
true | 63.33 | 0.54 | 2.0 | 8.0 | 4.0 | 0.585 | 1.0 | 1.0 | 3 | 1.0 | 2.0 | 180 | 1 | 0no
|
true | 23.75 | 0.415 | 1.0 | 8.0 | 4.0 | 0.04 | 0.0 | 1.0 | 2 | 0.0 | 2.0 | 128 | 7 | 0no
|
false | 20.75 | 10.25 | 2.0 | 11.0 | 4.0 | 0.71 | 1.0 | 1.0 | 2 | 1.0 | 2.0 | 49 | 1 | 1yes
|
false | 24.5 | 1.75 | 1.0 | 8.0 | 4.0 | 0.165 | 0.0 | 0.0 | 0 | 0.0 | 2.0 | 132 | 1 | 0no
|
true | 16.17 | 0.04 | 2.0 | 8.0 | 4.0 | 0.04 | 0.0 | 0.0 | 0 | 0.0 | 2.0 | 0 | 1 | 1yes
|
false | 29.5 | 2 | 1.0 | 10.0 | 8.0 | 2 | 0.0 | 0.0 | 0 | 0.0 | 2.0 | 256 | 18 | 0no
|
false | 52.83 | 15 | 2.0 | 8.0 | 4.0 | 5.5 | 1.0 | 1.0 | 14 | 0.0 | 2.0 | 0 | 2,201 | 1yes
|
true | 32.33 | 3.5 | 2.0 | 4.0 | 4.0 | 0.5 | 0.0 | 0.0 | 0 | 1.0 | 2.0 | 232 | 1 | 0no
|
true | 21.08 | 4.125 | 1.0 | 3.0 | 8.0 | 0.04 | 0.0 | 0.0 | 0 | 0.0 | 2.0 | 140 | 101 | 0no
|
true | 28.17 | 0.125 | 1.0 | 4.0 | 4.0 | 0.085 | 0.0 | 0.0 | 0 | 0.0 | 2.0 | 216 | 2,101 | 0no
|
true | 19 | 1.75 | 1.0 | 8.0 | 4.0 | 2.335 | 0.0 | 0.0 | 0 | 1.0 | 2.0 | 112 | 7 | 0no
|
true | 27.58 | 3.25 | 1.0 | 11.0 | 8.0 | 5.085 | 0.0 | 1.0 | 2 | 1.0 | 2.0 | 369 | 2 | 0no
|
true | 27.83 | 1.5 | 2.0 | 9.0 | 4.0 | 2 | 1.0 | 1.0 | 11 | 1.0 | 2.0 | 434 | 36 | 1yes
|
true | 40 | 6.5 | 2.0 | 6.0 | 5.0 | 3.5 | 1.0 | 1.0 | 1 | 0.0 | 2.0 | 0 | 501 | 1yes
|
false | 37.33 | 2.5 | 2.0 | 3.0 | 8.0 | 0.21 | 0.0 | 0.0 | 0 | 0.0 | 2.0 | 260 | 247 | 0no
|
true | 42.5 | 4.915 | 1.0 | 9.0 | 4.0 | 3.165 | 1.0 | 0.0 | 0 | 1.0 | 2.0 | 52 | 1,443 | 1yes
|
true | 56.75 | 12.25 | 2.0 | 7.0 | 4.0 | 1.25 | 1.0 | 1.0 | 4 | 1.0 | 2.0 | 200 | 1 | 1yes
|
true | 43.17 | 5 | 2.0 | 3.0 | 5.0 | 2.25 | 0.0 | 0.0 | 0 | 1.0 | 2.0 | 141 | 1 | 0no
|
false | 23.75 | 0.71 | 2.0 | 9.0 | 4.0 | 0.25 | 0.0 | 1.0 | 1 | 1.0 | 2.0 | 240 | 5 | 0no
|
true | 18.5 | 2 | 2.0 | 3.0 | 4.0 | 1.5 | 1.0 | 1.0 | 2 | 0.0 | 2.0 | 120 | 301 | 1yes
|
false | 40.83 | 3.5 | 2.0 | 3.0 | 5.0 | 0.5 | 0.0 | 0.0 | 0 | 0.0 | 1.0 | 1,160 | 1 | 0no
|
false | 24.5 | 0.5 | 2.0 | 11.0 | 8.0 | 1.5 | 1.0 | 0.0 | 0 | 0.0 | 2.0 | 280 | 825 | 1yes
|
true | 42 | 9.79 | 2.0 | 14.0 | 8.0 | 7.96 | 1.0 | 1.0 | 8 | 0.0 | 2.0 | 0 | 1 | 1yes
|
false | 19.5 | 0.165 | 2.0 | 11.0 | 4.0 | 0.04 | 0.0 | 0.0 | 0 | 1.0 | 2.0 | 380 | 1 | 0no
|
true | 21.5 | 11.5 | 2.0 | 3.0 | 4.0 | 0.5 | 1.0 | 0.0 | 0 | 1.0 | 2.0 | 100 | 69 | 0no
|
true | 31.25 | 2.835 | 2.0 | 1.0 | 1.0 | 0 | 0.0 | 1.0 | 5 | 0.0 | 2.0 | 176 | 147 | 0no
|
true | 27.25 | 1.585 | 2.0 | 13.0 | 8.0 | 1.835 | 1.0 | 1.0 | 12 | 1.0 | 2.0 | 583 | 714 | 1yes
|
true | 48.75 | 26.335 | 1.0 | 1.0 | 1.0 | 0 | 1.0 | 0.0 | 0 | 1.0 | 2.0 | 0 | 1 | 0no
|
false | 30.42 | 1.375 | 2.0 | 9.0 | 8.0 | 0.04 | 0.0 | 1.0 | 3 | 0.0 | 2.0 | 0 | 34 | 0no
|
true | 29.42 | 1.25 | 2.0 | 9.0 | 4.0 | 1.75 | 0.0 | 0.0 | 0 | 0.0 | 2.0 | 200 | 1 | 0no
|
true | 28.25 | 5.04 | 1.0 | 8.0 | 5.0 | 1.5 | 1.0 | 1.0 | 8 | 1.0 | 2.0 | 144 | 8 | 1yes
|
true | 40.25 | 21.5 | 2.0 | 10.0 | 9.0 | 20 | 1.0 | 1.0 | 11 | 0.0 | 2.0 | 0 | 1,201 | 1yes
|
true | 36.5 | 4.25 | 2.0 | 11.0 | 4.0 | 3.5 | 0.0 | 0.0 | 0 | 0.0 | 2.0 | 454 | 51 | 0no
|
true | 25.58 | 0.335 | 2.0 | 4.0 | 8.0 | 3.5 | 0.0 | 0.0 | 0 | 1.0 | 2.0 | 340 | 1 | 0no
|
true | 29.83 | 3.5 | 2.0 | 8.0 | 4.0 | 0.165 | 0.0 | 0.0 | 0 | 0.0 | 2.0 | 216 | 1 | 0no
|
true | 23.08 | 0 | 2.0 | 4.0 | 4.0 | 1 | 0.0 | 1.0 | 11 | 0.0 | 1.0 | 0 | 1 | 0no
|
false | 32.17 | 1.46 | 2.0 | 9.0 | 4.0 | 1.085 | 1.0 | 1.0 | 16 | 0.0 | 2.0 | 120 | 2,080 | 1yes
|
true | 25.17 | 3.5 | 2.0 | 13.0 | 4.0 | 0.625 | 1.0 | 1.0 | 7 | 0.0 | 2.0 | 0 | 7,060 | 1yes
|
false | 35.17 | 3.75 | 2.0 | 1.0 | 1.0 | 0 | 0.0 | 1.0 | 6 | 0.0 | 2.0 | 0 | 201 | 0no
|
false | 18.58 | 10 | 2.0 | 2.0 | 4.0 | 0.415 | 0.0 | 0.0 | 0 | 0.0 | 2.0 | 80 | 43 | 0no
|
true | 39.92 | 5 | 2.0 | 3.0 | 5.0 | 0.21 | 0.0 | 0.0 | 0 | 0.0 | 2.0 | 550 | 1 | 0no
|
true | 23.42 | 1 | 2.0 | 8.0 | 4.0 | 0.5 | 0.0 | 0.0 | 0 | 1.0 | 1.0 | 280 | 1 | 0no
|
true | 37.58 | 0 | 2.0 | 8.0 | 4.0 | 0 | 0.0 | 0.0 | 0 | 0.0 | 3.0 | 184 | 1 | 1yes
|
false | 24.75 | 13.665 | 2.0 | 11.0 | 8.0 | 1.5 | 0.0 | 0.0 | 0 | 0.0 | 2.0 | 280 | 2 | 0no
|
false | 47 | 13 | 2.0 | 3.0 | 5.0 | 5.165 | 1.0 | 1.0 | 9 | 1.0 | 2.0 | 0 | 1 | 1yes
|
true | 34.17 | 5.25 | 2.0 | 9.0 | 4.0 | 0.085 | 0.0 | 0.0 | 0 | 1.0 | 2.0 | 290 | 7 | 1yes
|
true | 22.17 | 0.585 | 1.0 | 1.0 | 1.0 | 0 | 0.0 | 0.0 | 0 | 0.0 | 2.0 | 100 | 1 | 0no
|
true | 27.75 | 1.29 | 2.0 | 4.0 | 8.0 | 0.25 | 0.0 | 0.0 | 0 | 1.0 | 1.0 | 140 | 1 | 0no
|
true | 42.75 | 4.085 | 2.0 | 6.0 | 4.0 | 0.04 | 0.0 | 0.0 | 0 | 0.0 | 2.0 | 108 | 101 | 0no
|
true | 28.67 | 14.5 | 2.0 | 2.0 | 4.0 | 0.125 | 0.0 | 0.0 | 0 | 0.0 | 2.0 | 0 | 287 | 0no
|
true | 36.25 | 5 | 2.0 | 8.0 | 5.0 | 2.5 | 1.0 | 1.0 | 6 | 0.0 | 2.0 | 0 | 368 | 1yes
|
false | 18.17 | 10 | 1.0 | 11.0 | 8.0 | 0.165 | 0.0 | 0.0 | 0 | 0.0 | 2.0 | 340 | 1 | 0no
|
true | 21.25 | 1.5 | 2.0 | 9.0 | 4.0 | 1.5 | 0.0 | 0.0 | 0 | 0.0 | 2.0 | 150 | 9 | 1yes
|
false | 38.92 | 1.665 | 2.0 | 6.0 | 4.0 | 0.25 | 0.0 | 0.0 | 0 | 0.0 | 2.0 | 0 | 391 | 0no
|
true | 31.83 | 0.04 | 1.0 | 7.0 | 4.0 | 0.04 | 0.0 | 0.0 | 0 | 0.0 | 2.0 | 0 | 1 | 0no
|
false | 17.33 | 9.5 | 2.0 | 6.0 | 4.0 | 1.75 | 0.0 | 1.0 | 10 | 1.0 | 2.0 | 0 | 11 | 0no
|
false | 20.42 | 0.835 | 2.0 | 11.0 | 4.0 | 1.585 | 1.0 | 1.0 | 1 | 0.0 | 2.0 | 0 | 1 | 1yes
|
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YAML Metadata
Error:
"configs[0]" must be of type object
Australian Credit
The Australian Credit from the UCI ML repository. Classification of loan approval.
Configurations and tasks
Configuration | Task | Description |
---|---|---|
australian_credit | Binary classification | Is the loan granted? |
Usage
from datasets import load_dataset
dataset = load_dataset("mstz/australian_credit")["train"]
Features
Target feature changes according to the selected configuration and is always in last position in the dataset.
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