I am trying to improve my understanding of how gbm works and how the model predicts out of sample.
More specifically, suppose you have a classification problem (two outcomes, 0 and 1). When you run a gbm model on a training set, observations are designated a predicted probability based on their characteristics. These predicted probabilities can be extracted quite easily.
Now, when you introduce out-of-sample data, how does gbm attempt to classify observations? Does it assign a predicted probability to observations based on similar values observed in the training set? If so, does gbm use a cutoff value? (i.e. predicted_probability>.5 implies a 1?) I am not sure if that's what gbm does since I cannot seem to find anywhere how to obtain these probabilities.
My goal is to construct an ROC curve from the predictions in the TEST SET, not on the training set, so if anyone has experience/insights about this would be great help