Can anyone help meevaluate testing set data in Weka

I got one training and testing dataset. I am using weka explorer, trying to create a model with Random forest (algorithm). After creating model when I use my testing set data to implement it by (supply test set/ re-evaluate on current dataset) tab, it showing some thing like that. Could anyone help me to find out the problem. Thanks

Training Model:

=== Evaluation on training set ===

Time taken to test model on training data: 0.24 seconds

=== Summary ===

Correctly Classified Instances        5243               98.9245 %
Incorrectly Classified Instances        57                1.0755 %
Kappa statistic                          0.9439
Mean absolute error                      0.0453
Root mean squared error                  0.1137
Relative absolute error                 23.2184 %
Root relative squared error             36.4074 %
Coverage of cases (0.95 level)         100      %
Mean rel. region size (0.95 level)      59.3019 %
Total Number of Instances             5300     

=== Detailed Accuracy By Class ===

             TP Rate  FP Rate  Precision  Recall   F-Measure  MCC      ROC   Area  PRC Area  Class
             0.996    0.067    0.992      0.996    0.994      0.944    0.999     1.000     0
             0.933    0.004    0.968      0.933    0.950      0.944    0.999     0.990     1
Weighted Avg.    0.989    0.060    0.989      0.989    0.989      0.944    0.999     0.999     

=== Confusion Matrix ===

    a    b   <-- classified as
 4702   18 |    a = 0
   39  541 |    b = 1

Model Implement on my testing dataset:

=== Evaluation on test set ===

Time taken to test model on supplied test set: 0.22 seconds

=== Summary ===

Total Number of Instances                0     
Ignored Class Unknown Instances               4000     

=== Detailed Accuracy By Class ===

             TP Rate  FP Rate  Precision  Recall   F-Measure  MCC      ROC  Area  PRC Area  Class
             0.000    0.000    0.000      0.000    0.000      0.000    ?         ?         0
             0.000    0.000    0.000      0.000    0.000      0.000    ?         ?         1

Weighted Avg. NaN NaN NaN NaN NaN NaN NaN NaN

=== Confusion Matrix ===

 a b   <-- classified as
 0 0 | a = 0
 0 0 | b = 1