Why would removing a variable in adaboost decrease error rate? 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.
Imagine that one of the column is just random data -- then it's not informative at all, so no classifier will be improved by including it.

However, ada's stochastic boosting implementations will always have some chance of including that variable in the classifier it generates. As a result, removing it has the potential to improve the classifiers generated.

(In your case, you might check whether that variable is part of the final model generated.)