title: Caret: There were missing values in resampled performance measures
body: <p>I am running caret's neural network on the Bike Sharing dataset and I get the following error message:</p>
  
  <blockquote>
    <p>In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
    :   There were missing values in resampled performance measures.</p>
  </blockquote>
  
  <p>I am not sure what the problem is. Can anyone help please? </p>
  
  <p>The dataset is from:
  <a href="https://archive.ics.uci.edu/ml/datasets/bike+sharing+dataset" rel="nofollow noreferrer">https://archive.ics.uci.edu/ml/datasets/bike+sharing+dataset</a></p>
  
  <p>Here is the coding:</p>
  
  <pre><code>library(caret)
  library(bestNormalize)
  
  data_hour = read.csv("hour.csv")
  
  # Split dataset
  set.seed(3)
  split = createDataPartition(data_hour$casual, p=0.80, list=FALSE)    
  validation = data_hour[-split,]
  dataset = data_hour[split,]
  dataset = dataset[,c(-1,-2,-4)]  
  
  # View strucutre of data
  str(dataset)
  
  # 'data.frame': 13905 obs. of  14 variables:
  # $ season    : int  1 1 1 1 1 1 1 1 1 1 ...
  # $ mnth      : int  1 1 1 1 1 1 1 1 1 1 ...
  # $ hr        : int  1 2 3 5 8 10 11 12 14 15 ...
  # $ holiday   : int  0 0 0 0 0 0 0 0 0 0 ...
  # $ weekday   : int  6 6 6 6 6 6 6 6 6 6 ...
  # $ workingday: int  0 0 0 0 0 0 0 0 0 0 ...
  # $ weathersit: int  1 1 1 2 1 1 1 1 2 2 ...
  # $ temp      : num  0.22 0.22 0.24 0.24 0.24 0.38 0.36 0.42 0.46 0.44 ...
  # $ atemp     : num  0.273 0.273 0.288 0.258 0.288 ...
  # $ hum       : num  0.8 0.8 0.75 0.75 0.75 0.76 0.81 0.77 0.72 0.77 ...
  # $ windspeed : num  0 0 0 0.0896 0 ...
  # $ casual    : int  8 5 3 0 1 12 26 29 35 40 ...
  # $ registered: int  32 27 10 1 7 24 30 55 71 70 ...
  # $ cnt       : int  40 32 13 1 8 36 56 84 106 110 ...
  
  ## transform numeric data to Guassian
  dataset_selected = dataset[,c(-13,-14)]                                                
  for (i in 8:12) { dataset_selected[,i] = predict(boxcox(dataset_selected[,i]   +0.1))}  
  
  # View transformed dataset
  str(dataset_selected)
  
  #'data.frame':  13905 obs. of  12 variables:
  #' $ season    : int  1 1 1 1 1 1 1 1 1 1 ...
  #' $ mnth      : int  1 1 1 1 1 1 1 1 1 1 ...
  #' $ hr        : int  1 2 3 5 8 10 11 12 14 15 ...
  #' $ holiday   : int  0 0 0 0 0 0 0 0 0 0 ...
  #' $ weekday   : int  6 6 6 6 6 6 6 6 6 6 ...
  #' $ workingday: int  0 0 0 0 0 0 0 0 0 0 ...
  #' $ weathersit: int  1 1 1 2 1 1 1 1 2 2 ...
  #' $ temp      : num  -1.47 -1.47 -1.35 -1.35 -1.35 ...
  #' $ atemp     : num  -1.18 -1.18 -1.09 -1.27 -1.09 ...
  #' $ hum       : num  0.899 0.899 0.637 0.637 0.637 ...
  #' $ windspeed : num  -1.8 -1.8 -1.8 -0.787 -1.8 ...
  #' $ casual    : num  -0.361 -0.588 -0.81 -1.867 -1.208 ...
  
  
  # Train data with Neural Network model from caret
  control = trainControl(method = 'repeatedcv', number = 10, repeats =3)
  metric = 'RMSE'
  set.seed(3)
  fit = train(casual ~., data = dataset_selected, method = 'nnet', metric = metric, trControl = control, trace = FALSE)
  </code></pre>
  
  <p>Thanks for your help!</p>