我正在使用装有R和xgboost版本0.6-4的Windows 10笔记本电脑。运行以下代码时,我遇到了奇怪的错误。
xgb_params <- list("objective" = "count:poisson",
"eval_metric" = "rmse")
regression <- xgboost(data = training_fold,
label = y_training_fold,
nrounds = 10,
params = xgb_params)
Error in xgb.iter.update(bst$handle, dtrain, iteration - 1, obj) :
amalgamation/../src/objective/regression_obj.cc:190: Check failed:
label_correct PoissonRegression: label must be nonnegative
但是当我查看该标签的摘要时,它显示为:
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.1129 0.3387 0.7000 1.0987 1.5265 4.5405 287
我该怎么修复这个问题?我试着删除了NA值,但是没有起作用。
谢谢提前!
编辑
这是traindata的样本。
dput(droplevels(head(train[, c(1,2,4,5,6,8,9,10,11)], 20)))
structure(list(VacancyId = structure(c(1L, 1L, 1L, 1L, 2L, 2L,
3L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L), .Label = c("55288","56838", "57822", "57902", "57925", "58008"), class = "factor"),
VacancyBankId = c(2L, 1609L, 1611L, 147L, 17L, 1611L, 2L,
257L, 1611L, 2L, 147L, 17L, 1611L, 239L, 1609L, 2L, 1609L,
2L, 2L, 1609L), FunctionId = c(36L, 36L, 36L, 36L, 35L, 35L,
3L, 4L, 4L, 4L, 4L, 9L, 9L, 9L, 3L, 3L, 3L, 3L, 3L, 3L),
EducationLevel = c(6L, 6L, 6L, 6L, 6L, 6L, 4L, 6L, 6L, 6L,
6L, 4L, 4L, 4L, 6L, 6L, 6L, 6L, 6L, 6L), ProvinceId = c(22L,
22L, 22L, 22L, 24L, 24L, 19L, 16L, 16L, 16L, 16L, 19L, 19L,
19L, 21L, 21L, 16L, 16L, 22L, 22L), CandidatesCount = c(126L,
27L, 18L, 12L, 1L, 4L, 2L, 6L, 7L, 7L, 1L, 8L, 15L, 13L,
7L, 7L, 7L, 7L, 7L, 7L), DurationDays = c(62L, 62L, 62L,
62L, 18L, 18L, 43L, 61L, 61L, 61L, 61L, 60L, 60L, 60L, 62L,
62L, 62L, 62L, 62L, 62L), DurationWeeks = c(8.857142857,
8.857142857, 8.857142857, 8.857142857, 2.571428571, 2.571428571,
6.142857143, 8.714285714, 8.714285714, 8.714285714, 8.714285714,
8.571428571, 8.571428571, 8.571428571, 8.857142857, 8.857142857,
8.857142857, 8.857142857, 8.857142857, 8.857142857), CandidatesPerWeek = c(NA,
3.048387097, 2.032258065, 1.35483871, 0.388888889, 1.555555556,
0.325581395, 0.68852459, 0.803278689, 0.803278689, 0.114754098,
0.933333333, 1.75, 1.516666667, 0.790322581, 0.790322581,
0.790322581, 0.790322581, 0.790322581, 0.790322581)), .Names = c("VacancyId", "VacancyBankId", "FunctionId", "EducationLevel", "ProvinceId", "CandidatesCount", "DurationDays", "DurationWeeks", "CandidatesPerWeek"), row.names = c(1L, 2L, 3L, 4L, 5L, 6L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 26L, 27L, 28L, 29L, 30L, 31L), class = "data.frame")
我希望能够通过FunctionId、Educationlevel、Province和VacancyBankId来预测每周的候选人数量。因此,y_training_fold表示每周的候选人数量,training_fold则表示职能、教育水平、省份和空缺银行ID。希望有人能帮助我!