r语言 - 为什么我得到:将 mlr 与 xgboost 一起使用时，标签的长度必须等于输入数据错误中的行数

Question

运行以下 R 代码后：

#' load libraries
library(parallelMap)
library(mlr)
#' *** Define the task
task = makeClassifTask(id = "classif_prem", 
                       data = data, 
                       target = "Result")
#' *** Define the learner
lrn = makeLearner(id = "learn_prem", cl = "classif.xgboost")
#' train model
mod = train(learner = lrn, task = task)

运行 str（数据） 后，我得到：

> str(data)
Classes ‘tbl_df’ and 'data.frame':  210 obs. of  3 variables:
 $ Result  : Factor w/ 3 levels "Draw","Loss",..: 1 3 1 1 1 2 2 2 1 1 ...
 $ RankDiff: int  11 3 11 5 -14 11 -2 -4 5 -8 ...
 $ DiffDiff: num  1.5 -1 1.5 -1 -1 0 0 -1.5 0 -1.5 ...

任务摘要提供：

Supervised task: classif_prem
Type: classif
Target: Result
Observations: 210
Features:
numerics  factors  ordered 
       2        0        0 
Missings: FALSE
Has weights: FALSE
Has blocking: FALSE
Classes: 3
Draw Loss  Win 
  61   64   85 
Positive class: NA
Warning message:
drop ignored 

然后我得到错误：

Error in xgb.setinfo(dmat, names(p), p[[1]]) : 
The length of labels must equal to the number of rows in the input data
In addition: Warning message:
drop ignored 

任何帮助我避免此错误将不胜感激。谢谢。

Answer 1

非常感谢。额外的tbl_df类弄乱了 mlr 和 xgboost。我只做了一个数据帧，它起作用了。