Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties
When optimizing ML model, sub-models are generated at each loop. They all also project features of the training set. From them, the model's probability distribution was acquired and the uncertainty of the model prediction was evaluated.
Reference
Singh, A., Serbin, S. P., McNeil, B. E., Kingdon, C. C., & Townsend, P. A. (2015). Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties. Ecological Applications, 25(8), 2180-2197.