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Binary or binomial classification is the task of classifying the elements of a given set into two groups on the basis of a classification rule. Some typical binary classification tasks are: * medical testing to determine if a patient has certain disease or not – the classification property is the presence of the disease; * A "pass or fail" test method or quality control in factories; i.e. deciding if a specification has or has not been met: a Go/no go classification. *An item may have a qualitative property; it does or does not have a specified characteristic * information retrieval, namely deciding whether a page or an article should be in the result set of a search or not – the classification property is the relevance of the article, or the usefulness to the user. An important point is that in many practical binary classification problems, the two groups are not symmetric – rather than overall accuracy, the relative proportion of different types of errors is of interest. For example, in medical testing, a false positive (detecting a disease when it is not present) is considered differently from a false negative (not detecting a disease when it is present). Sometimes, classification tasks are trivial. Given 100 balls, some of them red and some blue, a human with normal color vision can easily separate them into red ones and blue ones. However, some tasks, like those in practical medicine, and those interesting from the computer science point of view, are far from trivial, and may produce faulty results if executed imprecisely. ==Statistical Binary Classification== Statistical classification in general is one of the problems studied in computer science, in order to automatically learn classification systems on the basis of training set of data containing observations whose category is already known and use the system to identify the category labeling of new observations. When there are only two possible labeling categories, the problem is known as statistical binary classification. Some of the methods commonly used for binary classification are: *Decision trees *Random forests *Bayesian networks *Support vector machines *Neural networks *Logistic regression Various binary classifiers have been developed over time and there is no clear winner as to which classifier performs the best. Different classifiers perform differently depending on the number of observations, the dimensionality of the feature vector, the noise in the data and various other factors. For e.g. random forests perform better than SVM classifiers for 3D point clouds. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「binary classification」の詳細全文を読む スポンサード リンク
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