Suppose you are given a dataset with 4 attributes (F1, F2, F3, and F4). The class label is contained in attribute F4.
Now you build a random forest classification model and you test its performance using 10-fold cross-validation. For building the model you have used all four attributes (F1, F2, F3, and F4).
The precision and recall of your experiment are both close to 100%. Is there anything that went wrong? Would you obtain similar performance if you used a decision tree instead?