Model Assessment and Selection

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Random forests and stochastic gradient boosting for predicting tree canopy cover: Comparing tuning processes and model performance (2016, methodology): “We randomly assigned 25% of the data from each region as a tuning set and 25% as independent test data, leaving 50% for model training, as suggested by Hastie et al. (2009).”
The Evaluation of Tools Used to Predict the Impact of Missense Variants Is Hindered by Two Types of Circularity (2015, methodology): “Type 1 circularity occurs because of an overlap between training and evaluation datasets, possibly resulting in overﬁtting [Hastie et al., 2009], meaning that a tool is highly tailored to a given dataset, but will perform worse on novel variants.”