The immunity functions robustness and opportuneness are the basic decision functions in info-gap decision theory.
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Note that should be the " raw " output of the classifier's decision function, not the predicted class label.
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There exists some number of possible ways F _ \ theta to model our data " X ", which our decision function can use to make decisions.
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In that approach, one instead has a decision function between two alternatives, often based on a test statistic, and computes the rate of type I and type II errors as " ? " and " ? ".
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The link between the two can be seen by observing that the decision function for naive Bayes ( in the binary case ) can be rewritten as " predict class C _ 1 if the odds of p ( C _ 1 \ mid \ mathbf { x } ) exceed those of p ( C _ 2 \ mid \ mathbf { x } ) ".
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