An Assessment of Accuracy, Error, and Conflict with Support Values from

This article is for experienced biostatisticians. Nevertheless, this is the interpretation for the layman:
When molecular biology theories are tested with real data we need to be cautious in reading bootstrap values if we are assuming an underestimation of the actual support. For example (my example is not in this article), if using a decision tree vs. logistic regression bayesian model, be cautious in how you assess the accuracy of your model since the decision-tree tends to understimate and bayesian models tend to overestimate.

I have found that to increase a classifier accuracy for a model, this type of distinction (non-parametric bootstrap values vs. Bayesian probabilities) is fundamental.