I'm using gradient boosting regression model (GBRT).
To evaluate this model, I use 10-fold cross validation, in each of which I set same param and compute the coefficient of determination as a measure of fitting.
However, I find that there exists a huge difference in coefficient of determination obtained from each fold, e.g., the coefficient of determination from
[ 0.95310245 0.89725342 0.886711 **0.97063794** 0.84182142 0.80870443 0.70535911 0.8888032 **0.42510782** 0.70421155]
Although the mean is 0.81 and std is 0.31, there is a fold in which the coefficient of determination is 0.4, while another fold is 0.97.
The only difference btw each fold is just the training & test data set, why does there exist such huge difference? Is such difference indicating that the performance of my model is not good?