What is the benefit of a test data set, especially for naive bayes estimator or decision tree construction?
When using a naive bayes classifier the probabilities are a fact. As far as I know there is nothing one could tune (like the weights in a neural net). So what is the purpose of the test data set? Simply to know if one can apply naive bayes or not?
Similiarly what is the benefit of the test data set when constructing a decision tree. We alread use the gini impurity to construct the best possibe decision tree and there is nothing we could do when we get bad results with the test data set.