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In statistics, classification is the problem of identifying which of a set of categories (sub-populations) an observation (or observations) belongs to. Examples are assigning a given email to the "spam" or "non-spam" class, and assigning a diagnosis to a given patient based on observed characteristics of the patient (sex, blood pressure, presence or absence of certain symptoms, etc.).

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rdf:type	Thing disease
rdfs:label	Statistical classification (en)
rdfs:comment	In statistics, classification is the problem of identifying which of a set of categories (sub-populations) an observation (or observations) belongs to. Examples are assigning a given email to the "spam" or "non-spam" class, and assigning a diagnosis to a given patient based on observed characteristics of the patient (sex, blood pressure, presence or absence of certain symptoms, etc.). (en)
rdfs:seeAlso	Cluster analysis
sameAs	Statistical classification Statistical classification
dbp:wikiPageUsesTemplate	dbt:Colbegin dbt:Colend dbt:Commons_category dbt:For dbt:Main dbt:More_footnotes dbt:Portal dbt:Reflist dbt:See_also dbt:Short_description dbt:Statistics
Subject	Classification algorithms Statistical classification Machine learning
gold:hypernym	Problem
prov:wasDerivedFrom	http://en.wikipedia.org/wiki/Statistical_classification?oldid=1065376077&ns=0
Wikipage page ID	1579244 (xsd:integer)
page length (characters) of wiki page	14496 (xsd:nonNegativeInteger)
Wikipage revision ID	1065376077 (xsd:integer)
has abstract	In statistics, classification is the problem of identifying which of a set of categories (sub-populations) an observation (or observations) belongs to. Examples are assigning a given email to the "spam" or "non-spam" class, and assigning a diagnosis to a given patient based on observed characteristics of the patient (sex, blood pressure, presence or absence of certain symptoms, etc.). Often, the individual observations are analyzed into a set of quantifiable properties, known variously as explanatory variables or features. 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 particular word in an email) or real-valued (e.g. a measurement of blood pressure). Other classifiers work by comparing observations to previous observations by means of a similarity or distance function. 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. 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. Other fields may use different terminology: e.g. in community ecology, the term "classification" normally refers to cluster analysis. (en)
foaf:isPrimaryTopicOf	http://en.wikipedia.org/wiki/Statistical_classification
is Wikipage redirect of	Algorithms for statistical classification Classification (machine learning) List of classification algorithms Classification analysis Classification in machine learning Classifier (machine learning) Classifier (mathematics) Pattern classification Stochastic discrimination Applications of statistical classification Statistical classification (machine learning) Statistical classifier
is Link from a Wikipage to another Wikipage of	List of statistics articles List of things named after Thomas Bayes Local case-control sampling Logistic model tree Logistic regression Machine learning Machine olfaction Mahalanobis distance Noisy text analytics Normalized compression distance ADALINE ADaMSoft AN/MPQ-64 Sentinel Acoustic seabed classification AdaBoost Adaptive resonance theory Adaptive website AiLive Algorithms for statistical classification Alpha Profiling Anomaly detection Apache Spark Aphelion (software) Artificial intelligence Artificial neural network Atmospheric sounding Audio analysis Autoencoder Automated essay scoring Automated planning and scheduling Averaged one-dependence estimators Backpropagation Bag-of-words model Bani K. Mallick Bayes classifier Bayes error rate Bayesian inference Beta skeleton Bhattacharyya distance Bias–variance tradeoff Binary classification Biostatistics Boosting (machine learning) Bootstrap aggregating Bootstrapping C4.5 algorithm CRM114 (program) Calculus on finite weighted graphs Calibration (statistics) Cascading classifiers Categorization CellCognition Cell counting Gene expression programming General regression neural network Generalized iterative scaling Generative model Geoffrey McLachlan Glossary of artificial intelligence Josef Kittler

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