Translation and analysis of words by ChatGPT artificial intelligence
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qualify selection - translation to English
Ex: Unfortunately documents which present dilemmas in the selection of author headings are present in even the smallest library collections.
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* book selection = selección de libros
* deselection = expurgo
* document selection = selección de documentos
* limit + selection = restringir una búsqueda
* make + selections = seleccionar
* materials selection = selección de material
* mate selection = selección de la pareja
* merchandise selection = selección de productos
* natural selection = selección natural
* preselection [pre-selection] = preselección
* qualify + selection = restringir una búsqueda
* selection aid = instrumento de selección
* selection criterion = criterio de selección
* selection of documents = selección de documentos
* selection policy = política de adquisiciones
* selection procedures = procedimiento de selección
* selection process = proceso de selección
* selection tools = herramientas para la selección
* stock selection = selección de fondos
* term selection = selección de términos
* undertake + selection = realizar una selección
Ex: Common facets may be listed anywhere in the schedule order, because they are facets that, although only listed once can be applied anywhere in the citation order, as required to qualify the concept to which they apply.
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* disqualify = descalificar
* qualify for = reunir las condiciones para, permitir, poder
* qualify + search = acotar una búsqueda, delimitar una búsqueda, restringir una búsqueda
* qualify + selection = restringir una búsqueda
Ex: Many prominent librarians believe that materials selection is the single element of librarianship which gives it credence as a profession.
Definition
Wikipedia
In machine learning and statistics, feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. Feature selection techniques are used for several reasons:
- simplification of models to make them easier to interpret by researchers/users,
- shorter training times,
- to avoid the curse of dimensionality,
- improve data's compatibility with a learning model class,
- encode inherent symmetries present in the input space.
The central premise when using a feature selection technique is that the data contains some features that are either redundant or irrelevant, and can thus be removed without incurring much loss of information. Redundant and irrelevant are two distinct notions, since one relevant feature may be redundant in the presence of another relevant feature with which it is strongly correlated.
Feature selection techniques should be distinguished from feature extraction. Feature extraction creates new features from functions of the original features, whereas feature selection returns a subset of the features. Feature selection techniques are often used in domains where there are many features and comparatively few samples (or data points). Archetypal cases for the application of feature selection include the analysis of written texts and DNA microarray data, where there are many thousands of features, and a few tens to hundreds of samples.
