# How can one use a validation set to reduce overfitting Naive Bayes?

What is the correct procedure for using a validation set to reduce overfitting?

Say I split the data 80:10:10 (training: validation:test). I train on the training set then get 90% accuracy. I apply this model to the validation set then get 20%. What do I do then?

How can the validation set be used to reduce overfitting especially with reference to Naïve Bayes?

Do not use fixed splits. For each calculation in loop do $$n$$ times splitting + evaluation and take the mean and std of errors. Gives you better impression about the stability of the results and effectiveness of your algorithm.