R train randomForest, prox=TRUE causing failure
<p>I am training a model using randomForest. The data has been scrubbed and reduced to the bare minimum because I kept getting the dreaded
"something is wrong; all the Accuracy metric values are missing" error message.</p>

<p>For hours I assumed there were sparse values in factor levels or something and so I categorized (combined levels), etc.</p>

<p>Finally, after all this reduction, it would still fail with the error message. Just for grins, I removed the prox=TRUE parameter from the train function. All of the sudden, I'm not getting the error message. Does anybody have any insight on exactly what this parameter is doing and why it would be causing the error?</p>

<p>Here are some code fragments:</p>

<pre><code>library(caret)
set.seed(121)
inTrain = createDataPartition(y = reducedData$loan_status, p = 0.7, list = FALSE)
training = reducedData[ inTrain,]
testing = reducedData[-inTrain,]

fitControl &lt;- trainControl(method = "cv", number = 5, allowParallel = TRUE)

modelFit &lt;- train(loan_status ~ ., data = training, method = "rf", trControl = fitControl)
</code></pre>

<p>Now, if I add back prox=TRUE in the train function arguments, it will cause the dreaded error message again. As long as that param isn't there, it runs.</p>

<pre><code>Something is wrong; all the Accuracy metric values are missing:
    Accuracy       Kappa    
 Min.   : NA   Min.   : NA  
 1st Qu.: NA   1st Qu.: NA  
 Median : NA   Median : NA  
 Mean   :NaN   Mean   :NaN  
 3rd Qu.: NA   3rd Qu.: NA  
 Max.   : NA   Max.   : NA  
 NA's   :3     NA's   :3    
</code></pre>