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• Statistical_classification abstract "In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known. The individual observations are analyzed into a set of quantifiable properties, known as various explanatory variables, features, etc. These properties may variously be categorical (e.g. "A", "B", "AB" or "O", for blood type), ordinal (e.g. "large", "medium" or "small"), integer-valued (e.g. the number of occurrences of a part word in an email) or real-valued (e.g. a measurement of blood pressure). Some algorithms work only in terms of discrete data and require that real-valued or integer-valued data be discretized into groups (e.g. less than 5, between 5 and 10, or greater than 10). An example would be assigning a given email into "spam" or "non-spam" classes or assigning a diagnosis to a given patient as described by observed characteristics of the patient (gender, blood pressure, presence or absence of certain symptoms, etc.).An algorithm that implements classification, especially in a concrete implementation, is known as a classifier. The term "classifier" sometimes also refers to the mathematical function, implemented by a classification algorithm, that maps input data to a category.In the terminology of machine learning, classification is considered an instance of supervised learning, i.e. learning where a training set of correctly identified observations is available. The corresponding unsupervised procedure is known as clustering or cluster analysis, and involves grouping data into categories based on some measure of inherent similarity (e.g. the distance between instances, considered as vectors in a multi-dimensional vector space).Terminology across fields is quite varied. In statistics, where classification is often done with logistic regression or a similar procedure, the properties of observations are termed explanatory variables (or independent variables, regressors, etc.), and the categories to be predicted are known as outcomes, which are considered to be possible values of the dependent variable. In machine learning, the observations are often known as instances, the explanatory variables are termed features (grouped into a feature vector), and the possible categories to be predicted are classes. There is also some argument over whether classification methods that do not involve a statistical model can be considered "statistical". Other fields may use different terminology: e.g. in community ecology, the term "classification" normally refers to cluster analysis, i.e. a type of unsupervised learning, rather than the supervised learning described in this article.".
• Statistical_classification wikiPageExternalLink classifier-showdown.
• Statistical_classification wikiPageExternalLink stprtool.
• Statistical_classification wikiPageExternalLink libagf.sourceforge.net.
• Statistical_classification wikiPageExternalLink tooldiag.
• Statistical_classification wikiPageExternalLink KNN3.html.
• Statistical_classification wikiPageExternalLink classification-suite-jw.
• Statistical_classification wikiPageID "1579244".
• Statistical_classification wikiPageRevisionID "599039915".
• Statistical_classification hasPhotoCollection Statistical_classification.
• Statistical_classification subject Category:Classification_algorithms.
• Statistical_classification subject Category:Machine_learning.
• Statistical_classification subject Category:Statistical_classification.
• Statistical_classification type Abstraction100002137.
• Statistical_classification type Act100030358.
• Statistical_classification type Activity100407535.
• Statistical_classification type Algorithm105847438.
• Statistical_classification type ClassificationAlgorithms.
• Statistical_classification type Event100029378.
• Statistical_classification type Procedure101023820.
• Statistical_classification type PsychologicalFeature100023100.
• Statistical_classification type Rule105846932.
• Statistical_classification type YagoPermanentlyLocatedEntity.
• Statistical_classification comment "In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known. The individual observations are analyzed into a set of quantifiable properties, known as various explanatory variables, features, etc. These properties may variously be categorical (e.g.".
• Statistical_classification label "Classification automatique".
• Statistical_classification label "Classificazione statistica".
• Statistical_classification label "Klassifikationsverfahren".
• Statistical_classification label "Klasyfikacja statystyczna".
• Statistical_classification label "Statistical classification".
• Statistical_classification label "Задача классификации".
• Statistical_classification label "تصنيف إحصائي".
• Statistical_classification label "分类问题".
• Statistical_classification label "統計分類".
• Statistical_classification sameAs Klassifikationsverfahren.
• Statistical_classification sameAs Classification_automatique.
• Statistical_classification sameAs Classificazione_statistica.
• Statistical_classification sameAs 統計分類.
• Statistical_classification sameAs Klasyfikacja_statystyczna.
• Statistical_classification sameAs m.05czq1.
• Statistical_classification sameAs Q1744628.
• Statistical_classification sameAs Q1744628.
• Statistical_classification sameAs Statistical_classification.
• Statistical_classification wasDerivedFrom Statistical_classification?oldid=599039915.
• Statistical_classification isPrimaryTopicOf Statistical_classification.