I was trying to classify an outcome on some data using adaboost (the ada package in R) and I was playing around with the training data set of descriptors when I realized that removing a column in the descriptor matrix increased the accuracy of the output on the training data. Specifically, the number of false negatives dropped/true positives increased.
Aside from removing a single column in the descriptors, I left everything else the same, including number of iterations.