mimic having an independent test set by removing say 10% of the training data, developing the algorithm on the remaining 90%, and testing on the removed 10%. This is cross-validation, and can be carried out systematically by removing 10% in turn and repeating the procedure ten times, a procedure known as tenfold cross-validation.
