= Misclassification rate
{title2=$\mathbb P(\widehat g(X)\ne Y)$}
= Misclassification error
{synonym}
The misclassification rate is the expected <zero-one loss> of a <classification in statistical learning> rule. Its empirical version is the fraction of incorrectly labeled observations. Evaluation on the fitting sample gives a <training error>; evaluation on an independent <test set> estimates <prediction error>. For a majority-class leaf of a <classification tree>, the training error count is $n_t-\max_kn_{tk}$, unlike <classification-tree deviance>, which also uses the fitted class <probabilities>.
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