title: Boosting multiple class classification trees in R
body: <p>I'm trying to use gbm in R to create a boosting classification tree model for my data. 
   The problem is that I'm trying to classify my data into multiple labels and the only classification distribution I can find for gbm ("bernoulli") only works for binary classification. 
     Is there some change that I could make to my code to create a model which classifies the data into more than just two classes? </p>
  
  <pre><code>boost=gbm(label~., data=training, distribution="bernoulli", 
  n.trees=5000, 
  interaction.depth=4)
  </code></pre>