我知道这个问题已经被问过多次了，但是我已经耗尽了使模型起作用的想法。火车数据的前50行：
> train[1:25]
a b c d e f g h i j k l m
1: 0 148.00 27 16 0 A 0 117 92 0 13 271 2
2: 0 207.00 37 8 0 C 0 46 29 0 29 555 5
3: 0 1497.00 44 1 0 A 1 3754 2119 1 1961 5876 6
4: 0 463.00 44 1 0 A 0 287 202 0 105 1037 4
5: 0 19.00 82 1 0 A 0 301 186 0 344 2116 3
6: 0 204.00 41 1 0 A 0 92 76 0 290 1608 10
7: 0 79.00 69 16 0 B 0 48 29 0 1 27 3
8: 0 256.75 71 16 1 A 0 131 112 0 36 1183 0
9: 0 256.75 71 16 1 A 0 131 112 0 36 1183 2
10: 1 49.00 13 13 0 C 0 5 4 0 0 11 1
11: 0 19.00 76 1 0 A 0 897 440 0 575 2674 3
12: 0 49.00 100 100 0 C 0 6 6 0 0 0 1
13: 0 107.00 65 1 0 A 3 334 212 0 421 2773 6
14: 0 79.00 28 16 0 B 0 42 49 0 13 345 2
15: 0 1742.00 61 1 0 A 0 589 340 0 444 3853 8
16: 0 187.00 20 16 0 A 0 123 99 0 70 841 4
17: 0 68.00 73 1 0 A 0 757 507 0 359 773 3
18: 0 157.00 32 16 0 B 0 33 27 0 4 144 2
19: 0 49.00 52 16 0 C 0 10 7 0 2 51 3
20: 0 79.00 53 16 0 B 0 20 9 0 0 40 4
21: 0 68.00 45 1 0 A 0 370 245 0 298 1826 3
22: 0 1074.00 46 1 0 A 0 605 220 0 280 1421 7
23: 0 19.00 84 1 0 A 0 357 214 0 104 1273 3
24: 0 68.00 42 1 0 A 0 107 97 0 224 1526 3
25: 0 226.00 39 1 0 A 0 228 162 0 139 559 3
26: 0 49.00 92 16 0 C 0 4 3 0 0 0 3
27: 0 68.00 46 1 0 A 0 155 104 0 60 1170 3
28: 1 98.00 29 2 0 C 0 15 13 0 1 659 3
29: 0 248.00 44 1 0 A 0 347 204 0 281 1484 4
30: 0 19.00 84 1 0 A 0 302 166 0 170 2800 3
31: 0 444.00 20 16 0 A 0 569 411 1 369 1095 4
32: 0 157.00 20 16 0 B 0 38 30 0 18 265 3
33: 0 208.00 71 16 0 B 0 22 22 0 1 210 3
34: 1 84.00 27 13 0 A 0 37 24 0 1 649 1
35: 1 297.00 17 7 0 A 0 26 21 0 0 0 1
36: 1 49.00 43 16 1 C 0 4 4 0 0 0 2
37: 0 99.00 36 1 0 A 0 614 432 0 851 2839 4
38: 0 354.00 91 2 1 C 0 74 48 0 102 1005 9
39: 0 68.00 62 16 0 A 0 42 32 0 0 0 3
40: 0 49.00 78 16 0 C 0 12 10 0 0 95 3
41: 0 49.00 57 16 0 C 1 9 8 0 1 582 3
42: 0 68.00 49 1 0 A 0 64 47 0 49 112 3
43: 0 583.00 70 2 1 A 0 502 293 0 406 2734 9
44: 0 187.00 29 1 0 A 0 186 129 0 118 2746 5
45: 0 178.00 52 1 0 A 0 900 484 0 180 1701 4
46: 1 98.00 50 44 0 C 0 13 12 0 1 647 4
47: 1 548.00 21 14 0 A 0 19 14 0 0 0 1
48: 0 178.00 28 16 0 C 0 43 33 0 6 921 3
49: 1 49.00 20 20 0 C 0 8 6 0 0 0 1
50: 0 49.00 124 124 1 A 0 14 11 0 0 0 1
a b c d e f g h i j k l m
此数据尚未标准化，但在此阶段无关紧要。 我无法使用gbm包获得简单的gbm模型工作：
> require(gbm)
> gbm_model <- gbm(a ~ .
, data = train
, distribution="bernoulli"
, n.trees= 10
, shrinkage=0.001
, bag.fraction = 1
, train.fraction = 0.5
, n.minobsinnode = 3
, cv.folds = 0 # no cross-validation
, keep.data=TRUE
, verbose=TRUE
)
Iter TrainDeviance ValidDeviance StepSize Improve
1 nan nan 0.0010 nan
2 nan nan 0.0010 nan
3 nan nan 0.0010 nan
4 nan nan 0.0010 nan
5 nan nan 0.0010 nan
6 nan nan 0.0010 nan
7 nan nan 0.0010 nan
8 nan nan 0.0010 nan
9 nan nan 0.0010 nan
10 nan nan 0.0010 nan
列“ e”和“ f”是因素。火车数据样本大小约为6,000。我尝试使用各种bag.fraction，train.fraction，n.tree和收缩值来运行gbm，但仍然得到所有NaN相同的结果。树和SVM在相同数据上正常工作。我什至尝试将列'f'转换为字符，如前几篇文章中所建议的那样，但这没有用。
编辑：数据没有NA或无效值。我尝试对[f]列进行一次热编码，结果仍然相同。
答案 0 :(得分：0)
就我而言，此问题已通过将因变量转换为字符得到解决。
gbm_model <- gbm(as.character(a) ~ .
, data = train
, distribution="bernoulli"
, n.trees= 10
, shrinkage=0.001
, bag.fraction = 1
, train.fraction = 0.5
, n.minobsinnode = 3
, cv.folds = 0 # no cross-validation
, keep.data=TRUE
, verbose=TRUE
)