neuralnet in caret: wrong model type for classification
<p>I'm trying to train a model to predict a continuous numeric variable with the "neuralnet" method in the Caret package. When the below line of code is executed, the following error is thrown: </p>

<blockquote>
  <p>Error in train.default(Cadence_IVs, Cadence_Train_Response, method =
  "neuralnet",  :    wrong model type for classification</p>
</blockquote>

<pre><code>NN_Cadence &lt;- train(Cadence_IVs, Cadence_Train_Response, method = "neuralnet", layer1 = 10, layer2 = 5, decay = 0.1, linear.output = TRUE)
</code></pre>

<p>This is what the data looks likes, there are 105,000 rows:</p>

<pre><code>RiderID     Index      Date      Time  Average_Gradient  Max_Gradient   Distance   Highest_point  Speed         Power      Cadence
1           27330   3/28/2011  8:19:36       0              6.2          5132.29     12.8          47.9653271    63.3        71.5 
15           991    1/29/2016  6:05:04     -1.5              0            242.9       52.3         10.5608695    267.2       72.6 
15           979    1/29/2016  6:51:19       0               0           581.97      -23           10.03396552   239.2       77.6 
12          49047   4/14/2013  7:45:52       0              3.5           471.2       45.4         18.848        383.6       140.4 
11          46677   5/30/2015  15:25:44    -7.8            -2.6           410.7       124.4        18.66818182   98.3        97.9 
</code></pre>

<p>"RiderID" is coded as a Factor, and "Date" is coded as a Date variable. Time is coded as a Character but is excluded from Cadence_IVs. All the other variables are coded as "Numeric" data types, including the Response Variable which is "Cadence."</p>

<p>Cadence_IVs is a matrix of all the columns except for Cadence and Time. Cadence_Train_Response is a one column matrix of the values of Cadence.</p>

<p>Any help would be much appreciated. Let me know if I missed any details that might be helpful.</p>