Classification is supervised learningSupervised learningLearningsupervised that uses labeled dataLabeled dataDatalabeled to assign objects to classes. We distinguish false positive and false negative errors and review numerous indicators to quantify classifier performance. Also pairs of indicators are often considered to assess classification performance. We illustrate this with the receiver operating characteristic and the precision recall diagram. Several different classifiers with specific capabilities and limitations are presented in detail: the naive Bayes classifier, linear discriminant analysis, the support vector machine (SVM) using the kernel trick, nearest neighbor classifiers, learning vector quantification, and hierarchical classification using decision trees.