title: Invalid prediction for "rpart" object Error
body: <p>I am using the exact code for best first search from page 4 of this CRAN document (<a href="https://cran.r-project.org/web/packages/FSelector/FSelector.pdf" rel="nofollow" title="this CRAN document">https://cran.r-project.org/web/packages/FSelector/FSelector.pdf</a>), which uses the iris dataset. It works just fine on the iris dataset, but does not work on my ow ndata. My data has 37 predictor variables (both numerical and categorical) with the 38th column the Class prediction. </p>
  
  <p>I'm getting the error:</p>
  
  <pre><code>Error in predict.rpart(tree, test, type = "c") : 
     Invalid prediction for "rpart" object
  </code></pre>
  
  <p>Which I think comes from this line:</p>
  
  <pre><code>     error.rate = sum(test$Class != predict(tree, test, type="c")) / nrow(test)
  </code></pre>
  
  <p>I've tried the debug and traceback but I'm not understanding why this error is occurring (and like I said, it's not reproducible with iris data).</p>
  
  <p>Here's some of my data so you can see What I'm working with:</p>
  
  <pre><code>&gt; head(data)
  Numeric Binary Binary.1 Categorical Binary.2 Numeric.1 Numeric.2 Numeric.3     Numeric.4 Numeric.5 Numeric.6
  1      42      1        0           1        0  27.38953  38.93202  27.09122  38.15687  9.798653  18.57313
  2      43      1        0           3        0  76.34071  75.18190  73.66722  72.39449 23.546124  54.29957
  3      67      0        0           1        0 485.87158 287.35052 471.58863 281.55261 73.454080 389.40092
  4      49      0        0           3        0 200.83924 171.77136 164.33999 137.13165 36.525225 122.74080
  5      42      1        1           2        0 421.56508 243.05138 388.66823 221.17644 57.803488 285.72923
  6      48      1        1           2        0  69.48605  68.86291  67.57764  66.68408 16.661986  43.27868
    Numeric.7 Numeric.8 Numeric.9 Numeric.10 Numeric.11 Numeric.12 Numeric.13 Numeric.14 Numeric.15 Numeric.16
  1    1.9410    1.6244    1.4063   3.761285   11.07121   12.00510   1.631108   2.061702  0.7911462  1.0196401
  2    2.7874    2.4975    1.8621   4.519124   18.09848   15.46028   2.069787   2.650712  0.7808421  0.9650938
  3    4.9782    4.5829    4.0747  10.165202   24.66558   18.26303   2.266640   3.504340  0.6468095  1.8816444
  4    3.4169    3.0646    2.7983   7.275817   15.15534   13.93672   2.085589   2.309878  0.9028999  1.6726948
  5    5.2302    3.7912    3.4401   7.123413   59.64406   28.71171   3.311343   5.645815  0.5865128  0.8572746
  6    2.9730    2.2918    1.5164   4.541603   26.81567   18.67885   2.637904   3.523510  0.7486581  0.7908798
    Numeric.17 Numeric.18 Numeric.19 Numeric.20 Categorical.1 Numeric.21 Numeric.22 Numeric.23 Numeric.24
  1   2.145868   1.752803   64.91618  41.645192             1   9.703708   1.116614 0.09654643  4.0075897
  2   2.336676   1.933997   19.93420  11.824950             3  31.512059   1.360054 0.03559176  0.5806225
  3   5.473179   1.857276   44.22981  33.698516             1   8.498998       1.067967 0.04122081  0.7760942
  4   3.394066   2.143688   10.61420  29.636776             3  39.734071   1.549718 0.04577881  0.3102006
  5   1.744118   4.084250   38.28577  87.214615             2  59.519129   2.132184 0.16334461  0.3529899
  6   1.124962   4.037118   58.37065   3.894945             2  64.895248   2.190225 0.13461692  0.2672686
     Numeric.25 Numeric.26 Numeric.27 Numeric.28 Numeric.29 Numeric.30 Numeric.31 Class
  1 0.065523088   1.012919   1.331637 0.18721221  645.60854  144.49088  20.356321 FALSE
  2 0.030128214   1.182271   1.633734 0.10035377  206.18575  142.63844  24.376264 FALSE
  3 0.005638842   0.802835   1.172351 0.07512149   81.98983   91.44951  18.949937 FALSE
  4 0.061873262   1.323395   1.733104 0.12725994   51.14379  113.19654  28.529134 FALSE
  5 0.925931194   1.646710   3.096853 0.39408020  151.65062  103.64733       6.769099 FALSE
  6 0.548181302   1.767779   2.547693 0.34173633   46.10354  111.04652   9.658817 FALSE
  </code></pre>