Overfitting, but why is the training deviance dropping? The test set values increase over iterations signaling overfitting, but why is the training set deviance continuing to drop at the same time? This seems to indicate to me that the training set is continuing to get better over iterations but the test set only worsens, there is also a large gap in the starting points of the deviance between the test and training sets. Any clues as to where to look for causes would be helpful.


This is exactly what it means to overfit!
In many scenarios, you can make the training performance arbitrarily great, perhaps going as far as playing connect-the-dots. This is analogous to your training set curve decreasing. However, as you fit too tightly, you stop modeling the trend (signal) and start modeling coincidences in the training data that will not be present in the test data (noise).
