出了点问题；所有 RMSE 度量值都丢失了；使用插入符号火车功能
Something is wrong; all the RMSE metric values are missing; using caret train function
我正在尝试使用 caret 包拟合 gbm 模型。我知道其他人也有同样的问题，但这些问题的评论中提供的所有解决方案都无法解决我的错误。这是我的可重现代码：
library(dplyr)
library(MASS)
library(caret)
library(gbm)
Clean_winter_diff<-structure(list(Total = c(2L, 3L, 4L, 2L, 3L, 4L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 2L, 3L, 6L, 7L, 2L, 19L, 2L, 3L, 4L, 3L, 9L,
2L, 5L, 4L, 7L, 2L, 2L, 2L, 3L, 2L, 2L, 6L, 5L, 2L, 11L, 2L,
6L, 3L, 7L, 9L, 2L, 5L, 5L, 2L, 3L, 6L, 2L, 2L, 8L, 5L, 2L, 9L,
2L, 2L, 8L, 4L, 2L, 5L, 2L, 2L, 3L, 2L, 10L, 4L, 2L, 4L, 6L,
23L, 2L, 3L, 4L, 2L, 12L, 5L, 2L, 6L, 3L, 9L, 14L, 4L, 2L, 2L,
8L, 2L, 3L, 2L, 5L, 4L, 4L, 2L, 11L, 4L, 2L, 6L, 9L, 2L, 2L,
7L, 2L, 3L, 2L, 4L, 4L, 2L, 2L, 2L, 3L, 8L, 2L, 4L, 2L, 2L, 5L,
2L, 4L, 3L, 2L, 2L, 6L, 5L, 14L, 2L, 2L, 6L, 4L, 3L, 2L, 2L,
5L, 6L, 3L, 2L, 2L, 10L, 3L, 5L, 4L, 2L, 6L, 10L, 6L, 3L, 11L,
2L, 2L, 7L, 5L, 3L, 3L, 4L, 2L, 2L, 3L, 2L, 3L, 10L, 2L, 3L,
3L, 2L, 2L, 7L, 6L, 2L, 2L, 3L, 2L, 2L, 8L, 3L, 4L, 2L, 5L, 3L,
2L, 8L, 5L, 2L, 2L, 4L, 10L, 3L, 8L, 2L, 3L, 3L, 2L, 4L, 5L,
2L, 2L, 3L, 2L, 2L, 2L, 2L, 4L, 2L, 4L, 2L, 2L, 4L, 2L, 8L, 9L,
2L, 6L, 2L, 3L, 3L, 3L, 7L, 2L, 5L, 2L, 2L, 2L, 3L, 6L, 2L, 2L,
4L, 3L, 2L, 3L, 4L, 2L, 3L, 20L, 5L, 2L), Site = c(1, 1, 2, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1,
1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1,
1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2,
1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1),
Night = c(0, 0, 1, 0, 0, 0.25, 0, 0.666666667, 0.5, 0, 0,
0, 0, 0.5, 0, 0.5, 0.428571429, 0, 0.6315789, 1, 0.666666667,
0.25, 1, 0.333333333, 1, 0.2, 1, 0, 0, 0, 1, 0.666666667,
0, 0.5, 0.166666667, 0, 0.5, 0.909090909, 1, 0.333333333,
1, 0, 0.222222222, 0, 0, 0.2, 0, 0, 0.333333333, 0, 0.5,
0.375, 0, 0, 0.222222222, 0, 0, 0.25, 0, 0, 0, 0, 0, 0.666666667,
0, 0.2, 0.75, 0, 1, 1, 0.869565217, 0, 0, 0, 1, 1, 0.2, 0.5,
0.333333333, 0, 0, 1, 0.25, 0.5, 0, 0, 0.5, 0, 0, 0.4, 0,
0.75, 1, 0.363636364, 0, 1, 1, 0.222222222, 0.5, 0, 0.142857143,
0, 0, 0, 0.25, 0, 0, 1, 0, 0.333333333, 0.25, 0.5, 0, 0.5,
0, 0.4, 0.5, 0.25, 0, 0, 0, 0, 0, 0.785714286, 0, 0, 0.833333333,
0, 0, 0, 0, 1, 0.5, 0, 0.5, 0, 0.6, 0, 0.2, 0, 1, 0.166666667,
1, 0, 0.666666667, 1, 0, 0, 0.285714286, 0.2, 0, 0, 0, 0,
0.5, 0, 0.5, 0.666666667, 0.4, 1, 0, 0, 0, 1, 0.857142857,
1, 0.5, 0, 0.666666667, 0, 0.5, 0.5, 0, 0, 0, 0.2, 0.333333333,
0, 0, 0.2, 1, 1, 0.25, 0.8, 0.333333333, 0.5, 0, 0.666666667,
0.333333333, 0, 1, 0.6, 1, 0, 0.333333333, 0, 0.5, 0, 0,
0.75, 0, 0.5, 1, 1, 0, 1, 0.375, 0.666666667, 0.5, 0.333333333,
0, 0.666666667, 0, 0.333333333, 0.428571429, 0, 0.4, 0.5,
1, 0.5, 0.333333333, 0.5, 0.5, 1, 0.5, 0.666666667, 0.5,
1, 0.5, 0, 0.666666667, 0.5, 0.2, 1), Day = c(1, 1, 0, 1,
1, 0.75, 1, 0.333333333, 0.5, 1, 1, 1, 1, 0.5, 1, 0.5, 0.571428571,
1, 0.368421053, 0, 0.333333333, 0.75, 0, 0.666666667, 0,
0.8, 0, 1, 1, 1, 0, 0.333333333, 1, 0.5, 0.833333333, 1,
0.5, 0.090909091, 0, 0.666666667, 0, 1, 0.777777778, 1, 1,
0.8, 1, 1, 0.666666667, 1, 0.5, 0.625, 1, 1, 0.777777778,
1, 1, 0.75, 1, 1, 1, 1, 1, 0.333333333, 1, 0.8, 0.25, 1,
0, 0, 0.130434783, 1, 1, 1, 0, 0, 0.8, 0.5, 0.666666667,
1, 1, 0, 0.75, 0.5, 1, 1, 0.5, 1, 1, 0.6, 1, 0.25, 0, 0.636363636,
1, 0, 0, 0.777777778, 0.5, 1, 0.857142857, 1, 1, 1, 0.75,
1, 1, 0, 1, 0.666666667, 0.75, 0.5, 1, 0.5, 1, 0.6, 0.5,
0.75, 1, 1, 1, 1, 1, 0.214285714, 1, 1, 0.166666667, 1, 1,
1, 1, 0, 0.5, 1, 0.5, 1, 0.4, 1, 0.8, 1, 0, 0.833333333,
0, 1, 0.333333333, 0, 1, 1, 0.714285714, 0.8, 1, 1, 1, 1,
0.5, 1, 0.5, 0.333333333, 0.6, 0, 1, 1, 1, 0, 0.142857143,
0, 0.5, 1, 0.333333333, 1, 0.5, 0.5, 1, 1, 1, 0.8, 0.666666667,
1, 1, 0.8, 0, 0, 0.75, 0.2, 0.666666667, 0.5, 1, 0.333333333,
0.666666667, 1, 0, 0.4, 0, 1, 0.666666667, 1, 0.5, 1, 1,
0.25, 1, 0.5, 0, 0, 1, 0, 0.625, 0.333333333, 0.5, 0.666666667,
1, 0.333333333, 1, 0.666666667, 0.571428571, 1, 0.6, 0.5,
0, 0.5, 0.666666667, 0.5, 0.5, 0, 0.5, 0.333333333, 0.5,
0, 0.5, 1, 0.333333333, 0.5, 0.8, 0), Distance_forest = c(0.527747223,
0.680189568, 0, 0.310562619, 0.328173668, 0.278522078, 0.722954456,
0.784333633, 0.633598813, 0.106383899, 0.525329032, 0.246038608,
0.575318257, 0, 0.767179738, 0.443355317, 0.876859332, 0.19139315,
0, 0.037535778, 0.432922864, 0.131314978, 0, 0, 0.093159023,
0.128161967, 0, 0, 0.006470757, 0.30307544, 0, 0.568211372,
0.263593171, 0.131057648, 0.168134106, 0.367657292, 0.717686941,
0.163080941, 0, 0.202433621, 0.3842, 0, 0, 0.165167085, 0.929924705,
2.120840521, 0.484698725, 1.078311772, 0.366644583, 0.340810601,
0.298239859, 0.195581001, 0.02421172, 0, 0.464407271, 0.198840768,
0.054828399, 0.489438607, 0.295818359, 0.110773002, 0.496209018,
0.67346593, 0.214433884, 0.108712722, 0.529136166, 0.639769867,
0, 0.396732499, 0.483450073, 0.001882719, 0.248622382, 0.925764277,
0.175704519, 0.622952019, 0, 0, 1.142940058, 1.133076471,
0.224133662, 1.083342909, 0.745420612, 0.377062959, 0.08050045,
0.162178412, 1.361054023, 0.123874613, 0.49008657, 0.638751698,
0.167293055, 0.306236508, 0.581962136, 0.269203966, 0.01981849,
0.389124993, 0.333741945, 0.089434216, 0, 0.172470454, 0.174222306,
0.298973407, 0.139883014, 0.455618893, 0.612636301, 0.372548564,
0.35343891, 0.583316416, 0.291550392, 0.530795339, 0.07577014,
0.844212848, 0.106972082, 0.992915959, 0.044859616, 0.820739224,
0.799670156, 0.316242417, 0.319460412, 0.810118761, 0.500966406,
0.377834056, 0.940032033, 0.151399734, 0.28102882, 0.212952188,
0.073000622, 0.370545468, 0.872918616, 0, 0.104900131, 0.081847421,
0.216958479, 0.008668498, 0.007014128, 0.495791646, 0.02399882,
0.297470809, 0.490666846, 0.415433354, 0.301854897, 0.365931213,
0.692253337, 0.165305616, 0.640148893, 0.835302988, 0.768199373,
0.153852261, 0.134893226, 0.540233724, 0.335663076, 0.102341147,
0.195486707, 0.362254712, 0.324739821, 1.697227338, 0.520683209,
0.020203443, 0, 0.275300664, 0.259782193, 0.051199078, 0.217527413,
0.550995487, 0.656144105, 0.277954065, 0.091362713, 0.769716859,
0.817754331, 0.531972108, 0.330715097, 0.795027122, 0.818699405,
0.113381995, 0.73975023, 0.342823482, 0.760817657, 0.817530729,
0.700152145, 0.88797978, 0.29428625, 0.108928974, 0.074075782,
0.747234676, 0, 0.543069, 0.262442933, 0.262835131, 0.356383731,
0.371421971, 0.015478187, 0.601986047, 0, 0.048889129, 0.406113218,
0.127855407, 0.396601367, 0.294174095, 1.112770231, 0.066093385,
0.833489821, 0.27603216, 0.261494516, 0.139170942, 0.36716509,
0.303017066, 0.245362186, 0, 0.071559882, 0.08333732, 0.617973146,
0.075376835, 0.778806939, 0, 0.484474765, 0.09264197, 0.605744884,
0.568592372, 0.464302103, 0.219293483, 0.115301111, 0.636074027,
0.69132069, 0.448515825, 0.150593216, 0, 0.668861867, 0.664099955,
0.386919408, 0.568691441, 0.328245416, 0.441309029, 0.216574999,
0.191497106, 0.372996079, 0.211736755), Altitude_diff = c(-0.093344147,
-0.032953796, -0.166307236, -0.082168137, -0.074024556, 0.011625801,
-0.035469849, 0.023688222, -0.035174545, 0.009125112, -0.148026001,
-0.136813009, -0.140504929, -0.155278686, -0.141057312, -0.154625722,
-0.138962751, 0.021278778, -0.112632, -0.121742996, -0.104769694,
-0.062242187, -0.105238068, -0.118123369, -0.116926834, -0.057471783,
-0.099749664, -0.138632839, -0.086083588, -0.086340958, -0.109178192,
-0.09964916, -0.086616302, -0.113422317, -0.145193425, -0.139987988,
-0.12330925, -0.062, -0.073519485, -0.0852851, -0.087, -0.041133632,
-0.02300371, 0.145411285, 0.007278729, 0.043087274, 0.12858374,
0.074364258, 0.444998927, -0.018522705, -0.028386627, 0.007190659,
-0.045301581, -0.057804062, 0.132843404, 0.021017105, -0.078413605,
-0.046420864, 0.058002304, -0.081611237, 0.079912634, -0.050522034,
-0.024949936, -0.084849548, -0.062893188, -0.041188028, -0.051312736,
-0.01290921, -0.072736145, -0.079543025, -0.016072741, -0.019319687,
-0.0213343, 0.020119728, -0.071389999, -0.088737882, 0.073720496,
-0.019645096, -0.059846527, 0.08921346, -0.027587019, -0.064136113,
-0.06246801, -0.049053955, 0.119930542, 0.013316631, -0.060812866,
-0.010882792, -0.072900299, -0.00263418, 0.055887116, -0.057,
-0.152, -0.082, -0.134, -0.157, -0.117, -0.128, 0.022, -0.129,
0.121, 0.126, 0.091, -0.075, 0.014, -0.071, 0.009, -0.137,
-0.13, -0.131, -0.054, -0.132, -0.093, -0.134, -0.143, -0.127,
-0.089, -0.058, -0.057, -0.057, -0.055, -0.15, -0.17, -0.106,
-0.177, -0.009, 0.008, -0.08, 0.067, -0.131, -0.029, -0.016,
0.048, -0.154, -0.133, -0.109, -0.056, 0.029, -0.091, 0.031,
0.032, 0.022, 0.029, 0.06, 0.075, -0.099, 0.075, 0.202, -0.022,
0.013, 0.118, -0.022, -0.034, 0.224, -0.003, 0.095, 0.03,
0.04, 0.105, -0.013, 0.031, -0.038, -0.043, -0.01, 0.046,
-0.096, -0.028, -0.033, -0.023, 0.066, 0.063, -0.041, -0.001,
-0.005, -0.025, 0.047, -0.025, -0.028, -0.002, 0.065, -0.019,
-0.133, -0.045, 0.0479274, -0.0969804, -0.0511209, -0.1380578,
-0.0619915, -0.1375449, 0.028642, -0.139097, -0.0267313,
-0.0866448, -0.0664405, -0.0098812, 0.0950015, -0.0905839,
-0.1271573, -0.1345035, 0.0696888, 0.1161573, -0.001593,
-0.139097, -0.139097, -0.0351609, -0.1168084, -0.0487204,
-0.0427109, 0.0139613, -0.0361378, -0.073785, -0.0521353,
0.0207491, -0.0398732, -0.0512241, -0.0480128, -0.0133375,
-0.0047241, 0.0556789, -0.0389344, -0.0307192, -0.0410356,
-0.0436031, -0.0513303, 0.0914526, 0.108031, 0.078924, -0.0482411,
-0.0010576, -0.0543727, 0.1055158, -0.0347792, 0.0091985,
-0.0066721), Revisits = c(0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 1, 0, 2, 1, 0, 4, 0, 1, 2, 2, 1, 0, 2, 2, 1, 0, 0,
1, 1, 0, 1, 2, 1, 1, 5, 1, 2, 1, 3, 3, 0, 2, 1, 0, 0, 2,
0, 0, 1, 1, 1, 0, 0, 0, 2, 0, 0, 1, 0, 0, 2, 0, 7, 3, 1,
1, 3, 7, 0, 0, 1, 1, 7, 1, 0, 1, 1, 2, 9, 1, 1, 0, 2, 1,
0, 0, 0, 0, 1, 1, 3, 1, 1, 4, 2, 0, 0, 2, 0, 0, 0, 0, 0,
1, 1, 0, 1, 2, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 6, 0,
0, 2, 2, 0, 0, 0, 3, 0, 1, 0, 0, 2, 0, 0, 1, 0, 3, 3, 2,
2, 5, 0, 0, 4, 4, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0,
0, 4, 3, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1,
0, 4, 0, 1, 1, 1, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 1, 0, 1, 0, 1, 1, 0, 2, 0, 0, 0, 1, 4, 1, 2, 1, 0, 0,
0, 0, 1, 1, 0, 0, 0, 2, 2, 0, 0, 1, 2, 1), Ratio = c(0, 0,
2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 3, 7, 0, 4.75, 0,
3, 2, 1.5, 9, 0, 2.5, 2, 7, 0, 0, 2, 3, 0, 2, 3, 5, 2, 2.2,
2, 3, 3, 2.333333333, 3, 0, 2.5, 5, 0, 0, 3, 0, 0, 8, 5,
2, 0, 0, 0, 4, 0, 0, 5, 0, 0, 1.5, 0, 1.428571429, 1.333333333,
2, 4, 2, 3.285714286, 0, 0, 4, 2, 1.714285714, 5, 0, 6, 3,
4.5, 1.555555556, 4, 2, 0, 4, 2, 0, 0, 0, 0, 4, 2, 3.666666667,
4, 2, 1.5, 4.5, 0, 0, 3.5, 0, 0, 0, 0, 0, 2, 2, 0, 3, 4,
0, 4, 2, 0, 0, 2, 0, 3, 0, 0, 6, 5, 2.333333333, 0, 0, 3,
2, 0, 0, 0, 1.666666667, 0, 3, 0, 0, 5, 0, 0, 4, 0, 2, 3.333333333,
3, 1.5, 2.2, 0, 0, 1.75, 1.25, 3, 0, 4, 0, 2, 0, 2, 0, 0,
2, 0, 0, 0, 0, 1.75, 2, 2, 0, 3, 0, 0, 8, 0, 0, 0, 0, 3,
0, 8, 0, 2, 2, 0, 2.5, 3, 8, 2, 3, 3, 2, 4, 2.5, 2, 2, 3,
2, 2, 2, 2, 4, 2, 4, 2, 2, 4, 2, 8, 9, 2, 3, 2, 3, 3, 3,
1.75, 2, 2.5, 2, 2, 2, 3, 6, 2, 2, 4, 3, 2, 1.5, 2, 2, 3,
20, 2.5, 2), Area = c(0.032426, 0.035282, 0.113383, 0.035693,
0.041549, 0.058353, 0.031573, 0.057897, 0.034298, 0.075203,
0.038044, 0.039534, 0.035463, 0.056159, 0.0319, 0.152971,
0.063424, 0.033137, 0.184546, 0.054271, 0.043699, 0.070929,
0.086888, 0.182135, 0.055882, 0.063176, 0.072119, 0.1096,
0.035482, 0.040162, 0.056385, 0.042962, 0.032754, 0.062732,
0.056648, 0.035606, 0.062001, 0.117763, 0.062311, 0.089266,
0.078665, 0.091633, 0.065517, 0.037454, 0.060411, 0.073355,
0.035344, 0.033497, 0.119351, 0.044972, 0.031568, 0.114325,
0.068984, 0.061986, 0.109741, 0.033782, 0.031849, 0.105872,
0.055202, 0.031857, 0.064647, 0.031718, 0.032588, 0.076284,
0.036021, 0.216575, 0.100172, 0.06227, 0.060081, 0.063876,
0.224969, 0.045917, 0.037024, 0.077219, 0.054039, 0.158028,
0.067884, 0.034719, 0.120346, 0.044812, 0.080923, 0.171879,
0.069136, 0.0417, 0.032867, 0.11509, 0.053077, 0.062925,
0.033554, 0.07492, 0.114556, 0.096677, 0.049153, 0.161404,
0.073527, 0.045258, 0.08603, 0.091654, 0.033591, 0.033243,
0.060307, 0.048489, 0.041845, 0.031375, 0.046293, 0.034473,
0.044909, 0.052535, 0.060832, 0.082261, 0.086662, 0.031981,
0.053075, 0.057269, 0.031764, 0.039376, 0.061771, 0.051374,
0.081914, 0.04886, 0.040433, 0.056631, 0.086457, 0.118001,
0.033169, 0.033734, 0.064399, 0.065725, 0.043722, 0.062459,
0.032385, 0.07605, 0.055818, 0.067326, 0.034017, 0.033867,
0.052257, 0.062541, 0.073173, 0.069685, 0.034166, 0.096639,
0.081452, 0.116512, 0.064753, 0.12308, 0.033466, 0.050626,
0.068697, 0.105123, 0.066668, 0.075515, 0.076373, 0.046068,
0.032637, 0.067892, 0.059513, 0.032345, 0.076412, 0.055942,
0.057757, 0.070641, 0.038058, 0.04051, 0.049283, 0.063954,
0.040222, 0.043146, 0.062292, 0.05944, 0.032226, 0.121329,
0.086029, 0.040616, 0.033843, 0.037219, 0.066294, 0.034378,
0.117405, 0.095093, 0.032398, 0.062489, 0.060033, 0.0006219274,
0.0004771933187, 0.0005009547997, 0.0004406716919, 0.0005174510498,
0.0004356966248, 0.0006026420288, 0.0004355072708, 0.0005670226318,
0.0004853354187, 0.0005070045624, 0.0005619193115, 0.0006754835205,
0.0004834161072, 0.0004468427429, 0.000439496521, 0.0006436887817,
0.0006849831238, 0.0005693302002, 0.0004349030151, 0.0004349030151,
0.0005387456665, 0.0004572155151, 0.0005252477493, 0.0005314183146,
0.0005879613037, 0.0005381040955, 0.0005002150269, 0.0005234927775,
0.000592482015, 0.0005348047689, 0.0005223570905, 0.0005260328979,
0.0005637895386, 0.0005767995911, 0.000629678894, 0.0005354559326,
0.0005431971436, 0.0005328845113, 0.0005311777954, 0.0005214696045,
0.0006679819946, 0.0006827795207, 0.0006529239502, 0.0005282859904,
0.0005745828705, 0.0005196272583, 0.0006795158081, 0.0005336247467,
0.0005789768311, 0.0005680122375), Distance_main = c(1.131059754,
0.9597414435, 0, 1.256349606, 1.078548275, 1.855321885, 4.111540893,
5.445573732, 4.717162654, 3.192720443, 1.230485339, 4.582202671,
2.234386271, 4.464622586, 1.793303323, 3.049223638, 2.517519578,
2.538484406, 0.2589592261, 0.8107408556, 1.265087883, 2.583951508,
0.5704173619, 0.150727288, 0, 2.880491806, 0.4688362577,
1.032252927, 1.711598417, 2.621504704, 0.5018857525, 0.9121811232,
1.467942423, 0.5364545556, 1.956558175, 1.903428792, 1.556986206,
0.3888441615, 0.2643162488, 0.06508233719, 1.137, 1.050285586,
1.40077366, 3.600281886, 2.354502437, 1.899786116, 3.690234235,
2.808763349, 0.7511081312, 1.271708613, 2.662284706, 2.675257642,
3.518963652, 3.64493179, 2.047243432, 2.681735548, 3.55460067,
4.471868465, 4.870529144, 4.073487063, 3.088843029, 4.176214051,
3.878882256, 3.798820098, 3.638531617, 3.78621757, 3.517110032,
3.885770398, 3.298820012, 3.207448044, 3.236561986, 4.13860818,
5.461401614, 3.068585968, 2.839888067, 2.545155836, 2.390539028,
3.996152667, 2.813447134, 2.336287582, 3.609633571, 1.994576758,
2.756891326, 2.963835872, 2.077835347, 1.981514275, 1.698439482,
4.559660757, 1.832220975, 1.538482109, 0.4012068882, 1.011597874,
0.2762621903, 0.6604082443, 1.726855522, 0.4426442882, 1.389697061,
2.265330127, 4.673539548, 2.833166846, 3.247307991, 1.550221184,
1.913466888, 1.02140226, 1.419304966, 4.649917894, 3.021104929,
1.138684662, 0.9702250537, 0.8674368023, 1.363686091, 2.237998135,
3.078402963, 2.612860775, 2.659002418, 0.7922293863, 0.5605036917,
2.918464369, 2.607222198, 2.72011864, 3.293449501, 0.2339249027,
0.09269339846, 0.4948047539, 0.988393193, 3.35986433, 3.283307665,
0.4664049454, 3.579501178, 0.9978282525, 2.513329669, 1.751686648,
2.364558742, 0.3028119337, 0.2667488345, 0.5316889235, 4.034444068,
3.413510363, 0.5591667383, 3.303219295, 1.845610995, 2.029920015,
1.968676774, 1.642599316, 2.259782135, 1.840349328, 2.169684459,
1.466603062, 1.35662262, 1.287059026, 1.114386511, 0.1013909283,
0.5191928737, 2.069483497, 2.864063592, 3.741153421, 3.675316052,
2.612341652, 2.535722998, 4.374650663, 0.9801658265, 4.516729836,
4.200885496, 3.757806231, 2.911160806, 0.08124990183, 4.160713125,
4.82011578, 3.805524153, 2.356340037, 2.528406371, 2.849670115,
4.335904978, 2.334369917, 1.682493793, 0.9721257977, 2.886626751,
1.678288529, 3.207466146, 2.493581595, 1.024302173, 0.2878921523,
1.951664026, 0.001168478, 1.9688079e-05, 0.000181543742,
0.000169602217, 0.000342252497, 3.8581815e-05, 0.000831689834,
0, 0.000310111829, 0.000123848133, 0.00027892549, 0.000474703505,
0.000605096677, 0.001312503032, 0.000397102961, 0.001565818974,
0.001649622681, 0.0018610356, 0.001417062691, 0.000275126286,
0.000431104276, 0.003826022716, 8.0019175e-05, 0.004124439051,
0.004485276435, 0.004514712379, 0.00294698083, 0.001935731554,
0.002986659776, 0.002716345238, 0.002434957234, 0.002476156054,
0.001893628041, 0.001454772675, 0.00099942015, 0.001028825627,
0.001531671726, 0.001566268214, 0.001890167805, 0.000937548652,
0.000653203203, 0.000456625581, 0.001139386805, 0.001135244462,
0, 0.001190210739, 0.000552443287, 0.002855486907, 0.001430594014,
0.000594097595, 0.000339933191)), row.names = c(NA, -234L
), class = "data.frame")
mydata = transform(Clean_winter_diff, Site=Site-1)
#separating training and test data
alpha<-0.7
inTrain_diff <- sample(1:nrow(mydata), alpha * nrow(mydata))
train.set.diff <- mydata[inTrain_diff,]
test.set.diff <- mydata[-inTrain_diff,]
winter.boost=gbm(Site~. ,data = mydata,n.trees = 10000,
shrinkage = 0.01, interaction.depth = 6, cv.folds = 5, verbose = F)
best.iter=gbm.perf(winter.boost, method = "cv")
best.iter
summary(winter.boost)
#Using caret to get model performance in best iteration
set.seed(123)
fitControl = trainControl(method="cv", number=5, returnResamp = "all")
model2 = train(Site~., data=mydata[complete.cases(mydata),], method="gbm",distribution="bernoulli", trControl=fitControl, verbose=F, tuneGrid=data.frame(.n.trees=best.iter, .shrinkage=0.01, .interaction.depth=1, .n.minobsinnode=1))
这是我得到的错误：
RMSE Rsquared MAE
Min. : NA Min. : NA Min. : NA
1st Qu.: NA 1st Qu.: NA 1st Qu.: NA
Median : NA Median : NA Median : NA
Mean :NaN Mean :NaN Mean :NaN
3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA
Max. : NA Max. : NA Max. : NA
NA's :1 NA's :1 NA's :1
Error: Stopping
In addition: Warning messages:
1: In train.default(x, y, weights = w, ...) :
You are trying to do regression and your outcome only has two possible values Are you trying to do classification? If so, use a 2 level factor as your outcome column.
2: In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, :
There were missing values in resampled performance measures
我检查了数据框的缺失值，有 none。有什么问题？
sessionInfo():
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 17134)
Matrix products: default
locale:
[1] LC_COLLATE=Norwegian Bokmål_Norway.1252 LC_CTYPE=Norwegian Bokmål_Norway.1252 LC_MONETARY=Norwegian Bokmål_Norway.1252
[4] LC_NUMERIC=C LC_TIME=Norwegian Bokmål_Norway.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] caret_6.0-86 ggplot2_3.3.2 lattice_0.20-38 mlbench_2.1-1 gbm_2.1.8 MASS_7.3-51.4 randomForest_4.6-14
[8] tree_1.0-40 ISLR_1.2 dplyr_1.0.0 rpart.plot_3.0.8 rpart_4.1-15
loaded via a namespace (and not attached):
[1] tinytex_0.24 tidyselect_1.1.0 xfun_0.15 purrr_0.3.4 reshape2_1.4.4 splines_3.6.2
[7] colorspace_1.4-1 vctrs_0.3.1 generics_0.0.2 stats4_3.6.2 survival_3.1-8 prodlim_2019.11.13
[13] rlang_0.4.7 ModelMetrics_1.2.2.2 pillar_1.4.6 glue_1.4.1 withr_2.2.0 foreach_1.5.0
[19] lifecycle_0.2.0 plyr_1.8.6 lava_1.6.7 stringr_1.4.0 timeDate_3043.102 munsell_0.5.0
[25] gtable_0.3.0 recipes_0.1.13 codetools_0.2-16 parallel_3.6.2 class_7.3-15 Rcpp_1.0.5
[31] scales_1.1.1 ipred_0.9-9 stringi_1.4.6 grid_3.6.2 tools_3.6.2 magrittr_1.5
[37] tibble_3.0.3 crayon_1.3.4 pkgconfig_2.0.3 ellipsis_0.3.1 Matrix_1.2-18 data.table_1.13.0
[43] pROC_1.16.2 lubridate_1.7.9 gower_0.2.2 rstudioapi_0.11 iterators_1.0.12 R6_2.4.1
[49] nnet_7.3-12 nlme_3.1-142 compiler_3.6.2
>
您正在进行分类，因此您需要将因变量设置为 caret 中 train 起作用的因数：
set.seed(123)
fitControl = trainControl(method="cv", number=5, returnResamp = "all")
mydata$Site = factor(mydata$Site)
model2 = train(Site~., data=mydata[complete.cases(mydata),], method="gbm",distribution="bernoulli", trControl=fitControl, verbose=F, tuneGrid=data.frame(.n.trees=400, .shrinkage=0.01, .interaction.depth=1, .n.minobsinnode=1))
model2
Stochastic Gradient Boosting
234 samples
9 predictor
2 classes: '0', '1'
No pre-processing
Resampling: Cross-Validated (5 fold)
Summary of sample sizes: 187, 187, 187, 188, 187
Resampling results:
Accuracy Kappa
0.9232192 0.5550649
Tuning parameter 'n.trees' was held constant at a value
Tuning parameter 'n.minobsinnode' was held constant at
a value of 1
我正在尝试使用 caret 包拟合 gbm 模型。我知道其他人也有同样的问题，但这些问题的评论中提供的所有解决方案都无法解决我的错误。这是我的可重现代码：
library(dplyr)
library(MASS)
library(caret)
library(gbm)
Clean_winter_diff<-structure(list(Total = c(2L, 3L, 4L, 2L, 3L, 4L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 2L, 3L, 6L, 7L, 2L, 19L, 2L, 3L, 4L, 3L, 9L,
2L, 5L, 4L, 7L, 2L, 2L, 2L, 3L, 2L, 2L, 6L, 5L, 2L, 11L, 2L,
6L, 3L, 7L, 9L, 2L, 5L, 5L, 2L, 3L, 6L, 2L, 2L, 8L, 5L, 2L, 9L,
2L, 2L, 8L, 4L, 2L, 5L, 2L, 2L, 3L, 2L, 10L, 4L, 2L, 4L, 6L,
23L, 2L, 3L, 4L, 2L, 12L, 5L, 2L, 6L, 3L, 9L, 14L, 4L, 2L, 2L,
8L, 2L, 3L, 2L, 5L, 4L, 4L, 2L, 11L, 4L, 2L, 6L, 9L, 2L, 2L,
7L, 2L, 3L, 2L, 4L, 4L, 2L, 2L, 2L, 3L, 8L, 2L, 4L, 2L, 2L, 5L,
2L, 4L, 3L, 2L, 2L, 6L, 5L, 14L, 2L, 2L, 6L, 4L, 3L, 2L, 2L,
5L, 6L, 3L, 2L, 2L, 10L, 3L, 5L, 4L, 2L, 6L, 10L, 6L, 3L, 11L,
2L, 2L, 7L, 5L, 3L, 3L, 4L, 2L, 2L, 3L, 2L, 3L, 10L, 2L, 3L,
3L, 2L, 2L, 7L, 6L, 2L, 2L, 3L, 2L, 2L, 8L, 3L, 4L, 2L, 5L, 3L,
2L, 8L, 5L, 2L, 2L, 4L, 10L, 3L, 8L, 2L, 3L, 3L, 2L, 4L, 5L,
2L, 2L, 3L, 2L, 2L, 2L, 2L, 4L, 2L, 4L, 2L, 2L, 4L, 2L, 8L, 9L,
2L, 6L, 2L, 3L, 3L, 3L, 7L, 2L, 5L, 2L, 2L, 2L, 3L, 6L, 2L, 2L,
4L, 3L, 2L, 3L, 4L, 2L, 3L, 20L, 5L, 2L), Site = c(1, 1, 2, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1,
1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1,
1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2,
1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1),
Night = c(0, 0, 1, 0, 0, 0.25, 0, 0.666666667, 0.5, 0, 0,
0, 0, 0.5, 0, 0.5, 0.428571429, 0, 0.6315789, 1, 0.666666667,
0.25, 1, 0.333333333, 1, 0.2, 1, 0, 0, 0, 1, 0.666666667,
0, 0.5, 0.166666667, 0, 0.5, 0.909090909, 1, 0.333333333,
1, 0, 0.222222222, 0, 0, 0.2, 0, 0, 0.333333333, 0, 0.5,
0.375, 0, 0, 0.222222222, 0, 0, 0.25, 0, 0, 0, 0, 0, 0.666666667,
0, 0.2, 0.75, 0, 1, 1, 0.869565217, 0, 0, 0, 1, 1, 0.2, 0.5,
0.333333333, 0, 0, 1, 0.25, 0.5, 0, 0, 0.5, 0, 0, 0.4, 0,
0.75, 1, 0.363636364, 0, 1, 1, 0.222222222, 0.5, 0, 0.142857143,
0, 0, 0, 0.25, 0, 0, 1, 0, 0.333333333, 0.25, 0.5, 0, 0.5,
0, 0.4, 0.5, 0.25, 0, 0, 0, 0, 0, 0.785714286, 0, 0, 0.833333333,
0, 0, 0, 0, 1, 0.5, 0, 0.5, 0, 0.6, 0, 0.2, 0, 1, 0.166666667,
1, 0, 0.666666667, 1, 0, 0, 0.285714286, 0.2, 0, 0, 0, 0,
0.5, 0, 0.5, 0.666666667, 0.4, 1, 0, 0, 0, 1, 0.857142857,
1, 0.5, 0, 0.666666667, 0, 0.5, 0.5, 0, 0, 0, 0.2, 0.333333333,
0, 0, 0.2, 1, 1, 0.25, 0.8, 0.333333333, 0.5, 0, 0.666666667,
0.333333333, 0, 1, 0.6, 1, 0, 0.333333333, 0, 0.5, 0, 0,
0.75, 0, 0.5, 1, 1, 0, 1, 0.375, 0.666666667, 0.5, 0.333333333,
0, 0.666666667, 0, 0.333333333, 0.428571429, 0, 0.4, 0.5,
1, 0.5, 0.333333333, 0.5, 0.5, 1, 0.5, 0.666666667, 0.5,
1, 0.5, 0, 0.666666667, 0.5, 0.2, 1), Day = c(1, 1, 0, 1,
1, 0.75, 1, 0.333333333, 0.5, 1, 1, 1, 1, 0.5, 1, 0.5, 0.571428571,
1, 0.368421053, 0, 0.333333333, 0.75, 0, 0.666666667, 0,
0.8, 0, 1, 1, 1, 0, 0.333333333, 1, 0.5, 0.833333333, 1,
0.5, 0.090909091, 0, 0.666666667, 0, 1, 0.777777778, 1, 1,
0.8, 1, 1, 0.666666667, 1, 0.5, 0.625, 1, 1, 0.777777778,
1, 1, 0.75, 1, 1, 1, 1, 1, 0.333333333, 1, 0.8, 0.25, 1,
0, 0, 0.130434783, 1, 1, 1, 0, 0, 0.8, 0.5, 0.666666667,
1, 1, 0, 0.75, 0.5, 1, 1, 0.5, 1, 1, 0.6, 1, 0.25, 0, 0.636363636,
1, 0, 0, 0.777777778, 0.5, 1, 0.857142857, 1, 1, 1, 0.75,
1, 1, 0, 1, 0.666666667, 0.75, 0.5, 1, 0.5, 1, 0.6, 0.5,
0.75, 1, 1, 1, 1, 1, 0.214285714, 1, 1, 0.166666667, 1, 1,
1, 1, 0, 0.5, 1, 0.5, 1, 0.4, 1, 0.8, 1, 0, 0.833333333,
0, 1, 0.333333333, 0, 1, 1, 0.714285714, 0.8, 1, 1, 1, 1,
0.5, 1, 0.5, 0.333333333, 0.6, 0, 1, 1, 1, 0, 0.142857143,
0, 0.5, 1, 0.333333333, 1, 0.5, 0.5, 1, 1, 1, 0.8, 0.666666667,
1, 1, 0.8, 0, 0, 0.75, 0.2, 0.666666667, 0.5, 1, 0.333333333,
0.666666667, 1, 0, 0.4, 0, 1, 0.666666667, 1, 0.5, 1, 1,
0.25, 1, 0.5, 0, 0, 1, 0, 0.625, 0.333333333, 0.5, 0.666666667,
1, 0.333333333, 1, 0.666666667, 0.571428571, 1, 0.6, 0.5,
0, 0.5, 0.666666667, 0.5, 0.5, 0, 0.5, 0.333333333, 0.5,
0, 0.5, 1, 0.333333333, 0.5, 0.8, 0), Distance_forest = c(0.527747223,
0.680189568, 0, 0.310562619, 0.328173668, 0.278522078, 0.722954456,
0.784333633, 0.633598813, 0.106383899, 0.525329032, 0.246038608,
0.575318257, 0, 0.767179738, 0.443355317, 0.876859332, 0.19139315,
0, 0.037535778, 0.432922864, 0.131314978, 0, 0, 0.093159023,
0.128161967, 0, 0, 0.006470757, 0.30307544, 0, 0.568211372,
0.263593171, 0.131057648, 0.168134106, 0.367657292, 0.717686941,
0.163080941, 0, 0.202433621, 0.3842, 0, 0, 0.165167085, 0.929924705,
2.120840521, 0.484698725, 1.078311772, 0.366644583, 0.340810601,
0.298239859, 0.195581001, 0.02421172, 0, 0.464407271, 0.198840768,
0.054828399, 0.489438607, 0.295818359, 0.110773002, 0.496209018,
0.67346593, 0.214433884, 0.108712722, 0.529136166, 0.639769867,
0, 0.396732499, 0.483450073, 0.001882719, 0.248622382, 0.925764277,
0.175704519, 0.622952019, 0, 0, 1.142940058, 1.133076471,
0.224133662, 1.083342909, 0.745420612, 0.377062959, 0.08050045,
0.162178412, 1.361054023, 0.123874613, 0.49008657, 0.638751698,
0.167293055, 0.306236508, 0.581962136, 0.269203966, 0.01981849,
0.389124993, 0.333741945, 0.089434216, 0, 0.172470454, 0.174222306,
0.298973407, 0.139883014, 0.455618893, 0.612636301, 0.372548564,
0.35343891, 0.583316416, 0.291550392, 0.530795339, 0.07577014,
0.844212848, 0.106972082, 0.992915959, 0.044859616, 0.820739224,
0.799670156, 0.316242417, 0.319460412, 0.810118761, 0.500966406,
0.377834056, 0.940032033, 0.151399734, 0.28102882, 0.212952188,
0.073000622, 0.370545468, 0.872918616, 0, 0.104900131, 0.081847421,
0.216958479, 0.008668498, 0.007014128, 0.495791646, 0.02399882,
0.297470809, 0.490666846, 0.415433354, 0.301854897, 0.365931213,
0.692253337, 0.165305616, 0.640148893, 0.835302988, 0.768199373,
0.153852261, 0.134893226, 0.540233724, 0.335663076, 0.102341147,
0.195486707, 0.362254712, 0.324739821, 1.697227338, 0.520683209,
0.020203443, 0, 0.275300664, 0.259782193, 0.051199078, 0.217527413,
0.550995487, 0.656144105, 0.277954065, 0.091362713, 0.769716859,
0.817754331, 0.531972108, 0.330715097, 0.795027122, 0.818699405,
0.113381995, 0.73975023, 0.342823482, 0.760817657, 0.817530729,
0.700152145, 0.88797978, 0.29428625, 0.108928974, 0.074075782,
0.747234676, 0, 0.543069, 0.262442933, 0.262835131, 0.356383731,
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), class = "data.frame")
mydata = transform(Clean_winter_diff, Site=Site-1)
#separating training and test data
alpha<-0.7
inTrain_diff <- sample(1:nrow(mydata), alpha * nrow(mydata))
train.set.diff <- mydata[inTrain_diff,]
test.set.diff <- mydata[-inTrain_diff,]
winter.boost=gbm(Site~. ,data = mydata,n.trees = 10000,
shrinkage = 0.01, interaction.depth = 6, cv.folds = 5, verbose = F)
best.iter=gbm.perf(winter.boost, method = "cv")
best.iter
summary(winter.boost)
#Using caret to get model performance in best iteration
set.seed(123)
fitControl = trainControl(method="cv", number=5, returnResamp = "all")
model2 = train(Site~., data=mydata[complete.cases(mydata),], method="gbm",distribution="bernoulli", trControl=fitControl, verbose=F, tuneGrid=data.frame(.n.trees=best.iter, .shrinkage=0.01, .interaction.depth=1, .n.minobsinnode=1))
这是我得到的错误：
RMSE Rsquared MAE
Min. : NA Min. : NA Min. : NA
1st Qu.: NA 1st Qu.: NA 1st Qu.: NA
Median : NA Median : NA Median : NA
Mean :NaN Mean :NaN Mean :NaN
3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA
Max. : NA Max. : NA Max. : NA
NA's :1 NA's :1 NA's :1
Error: Stopping
In addition: Warning messages:
1: In train.default(x, y, weights = w, ...) :
You are trying to do regression and your outcome only has two possible values Are you trying to do classification? If so, use a 2 level factor as your outcome column.
2: In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, :
There were missing values in resampled performance measures
我检查了数据框的缺失值，有 none。有什么问题？
sessionInfo():
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 17134)
Matrix products: default
locale:
[1] LC_COLLATE=Norwegian Bokmål_Norway.1252 LC_CTYPE=Norwegian Bokmål_Norway.1252 LC_MONETARY=Norwegian Bokmål_Norway.1252
[4] LC_NUMERIC=C LC_TIME=Norwegian Bokmål_Norway.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] caret_6.0-86 ggplot2_3.3.2 lattice_0.20-38 mlbench_2.1-1 gbm_2.1.8 MASS_7.3-51.4 randomForest_4.6-14
[8] tree_1.0-40 ISLR_1.2 dplyr_1.0.0 rpart.plot_3.0.8 rpart_4.1-15
loaded via a namespace (and not attached):
[1] tinytex_0.24 tidyselect_1.1.0 xfun_0.15 purrr_0.3.4 reshape2_1.4.4 splines_3.6.2
[7] colorspace_1.4-1 vctrs_0.3.1 generics_0.0.2 stats4_3.6.2 survival_3.1-8 prodlim_2019.11.13
[13] rlang_0.4.7 ModelMetrics_1.2.2.2 pillar_1.4.6 glue_1.4.1 withr_2.2.0 foreach_1.5.0
[19] lifecycle_0.2.0 plyr_1.8.6 lava_1.6.7 stringr_1.4.0 timeDate_3043.102 munsell_0.5.0
[25] gtable_0.3.0 recipes_0.1.13 codetools_0.2-16 parallel_3.6.2 class_7.3-15 Rcpp_1.0.5
[31] scales_1.1.1 ipred_0.9-9 stringi_1.4.6 grid_3.6.2 tools_3.6.2 magrittr_1.5
[37] tibble_3.0.3 crayon_1.3.4 pkgconfig_2.0.3 ellipsis_0.3.1 Matrix_1.2-18 data.table_1.13.0
[43] pROC_1.16.2 lubridate_1.7.9 gower_0.2.2 rstudioapi_0.11 iterators_1.0.12 R6_2.4.1
[49] nnet_7.3-12 nlme_3.1-142 compiler_3.6.2
>
您正在进行分类，因此您需要将因变量设置为 caret 中 train 起作用的因数：
set.seed(123)
fitControl = trainControl(method="cv", number=5, returnResamp = "all")
mydata$Site = factor(mydata$Site)
model2 = train(Site~., data=mydata[complete.cases(mydata),], method="gbm",distribution="bernoulli", trControl=fitControl, verbose=F, tuneGrid=data.frame(.n.trees=400, .shrinkage=0.01, .interaction.depth=1, .n.minobsinnode=1))
model2
Stochastic Gradient Boosting
234 samples
9 predictor
2 classes: '0', '1'
No pre-processing
Resampling: Cross-Validated (5 fold)
Summary of sample sizes: 187, 187, 187, 188, 187
Resampling results:
Accuracy Kappa
0.9232192 0.5550649
Tuning parameter 'n.trees' was held constant at a value
Tuning parameter 'n.minobsinnode' was held constant at
a value of 1