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Classification is a form of data analysis that extracts models describing important data classes.
Effective and scalable methods have been developed for decision tree induction, Naive Bayesian classification, rule-based classification, and many other classification methods.
Evaluation metrics include: accuracy, sensitivity, specificity, precision, recall, F measure, and Fß measure.
Stratified k-fold cross-validation is recommended for accuracy estimation. Bagging and boosting can be used to increase overall accuracy by learning and combining a series of individual models.
Significance tests and ROC curves are useful for model selection.
There have been numerous comparisons of the different classification methods; the matter remains a research topic
No single method has been found to be superior over all others for all data sets
Issues such as accuracy, training time, robustness, scalability, and interpretability must be considered and can involve trade-offs, further complicating the quest for an overall superior method

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