The decision threshold of a binary classifier that outputs scores, such as decision trees or naive Bayes, is the value above which scores are interpreted as positive classifications. Decision thresholds can be either fixed if the classifier outputs calibrated scores on a known scale (e.g., 0.5 for a probabilistic classifier), or learned from data if the scores are uncalibrated. See ROC Analysis.
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(2017). Decision Threshold. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_203
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