Why do I get: The length of labels must equal the number of rows in the input data error while using mlr with xgboost
<p>After running the following R code:</p>

<pre><code>#' 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)
</code></pre>

<p>After running str(data) I get:</p>

<pre><code>&gt; 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 ...
</code></pre>

<p>The task summary gives:</p>

<pre><code>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 
</code></pre>

<p>I then get the error:</p>

<pre><code>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 
</code></pre>

<p>Any help for me to avoid this error would be appreciated. Thanks.</p>