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The ISO/IEC 11179 metadata registry standard uses classification schemes as a way to classify administered items, such as data elements, in a metadata registry. Some quality criteria for classification schemes are: Whether different kinds are grouped together. In other words, whether it is a grouping system or a pure classification system.
Pragmatic classification (and functional [39] and teleological classification) is the classification of items which emphasis the goals, purposes, consequences, [40] interests, values and politics of classification. It is, for example, classifying animals into wild animals, pests, domesticated animals and pets.
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.
Standard examples of each, all of which are linear classifiers, are: generative classifiers: naive Bayes classifier and; linear discriminant analysis; discriminative model: logistic regression; In application to classification, one wishes to go from an observation x to a label y (or probability distribution on labels
The concept of data type is similar to the concept of level of measurement, but more specific. For example, count data requires a different distribution (e.g. a Poisson distribution or binomial distribution) than non-negative real-valued data require, but both fall under the same level of measurement (a ratio scale).
Another example of a faceted classification scheme is the Universal Decimal Classification (UDC), a complex multilingual classification that can be used in all fields of knowledge. [12] The Universal Decimal Classification scheme was created at the end of the nineteenth century by Belgian bibliographers Paul Otlet and Henri la Fontaine .
Exploratory data analysis is an analysis technique to analyze and investigate the data set and summarize the main characteristics of the dataset. Main advantage of EDA is providing the data visualization of data after conducting the analysis.
To evaluate a classifier, one compares its output to another reference classification – ideally a perfect classification, but in practice the output of another gold standard test – and cross tabulates the data into a 2×2 contingency table, comparing the two classifications.