Fig. 5: Impact of individual features on model prediction accuracy.
From: The origin and evolution of open habitats in North America inferred by Bayesian deep learning models
The displayed delta-accuracy values (y-axis) constitute a measure of how important a given feature is for the trained model to make accurate vegetation predictions. This is determined by measuring the drop in prediction accuracy when the information content of a given feature is removed (permutation feature importance). High delta accuracy values indicate high feature importance. Points show the mean delta-accuracy of each feature across 100 randomly selected posterior BNN weight samples. The inserted panel (“All features”) displays an overview of the delta-accuracy estimates for all 108 features, while the main panel displays only the most important features for the trained model. Note that the feature importance determined in this way is not an absolute measure of how important a given predictor is for the task of vegetation prediction, but rather it is an assessment of how much a given model relies on a given predictor. The identity of the most important features may change depending on the model architectures, even when based on the same data. However, the most important features identified in this manner are expected to contain relevant information for the given task, in this case for reconstructing vegetation. Source data are provided as a Source data file.
