Feature selectionFeature selection is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. Stylometry and DNA microarray analysis are two cases where feature selection is used. It should be distinguished from feature extraction. 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.
Personnel selectionPersonnel selection is the methodical process used to hire (or, less commonly, promote) individuals. Although the term can apply to all aspects of the process (recruitment, selection, hiring, onboarding, acculturation, etc.) the most common meaning focuses on the selection of workers. In this respect, selected prospects are separated from rejected applicants with the intention of choosing the person who will be the most successful and make the most valuable contributions to the organization.
RecruitmentRecruitment is the overall process of identifying, sourcing, screening, shortlisting, and interviewing candidates for jobs (either permanent or temporary) within an organization. Recruitment also is the process involved in choosing people for unpaid roles. Managers, human resource generalists and recruitment specialists may be tasked with carrying out recruitment, but in some cases public-sector employment, commercial recruitment agencies, or specialist search consultancies are used to undertake parts of the process.
Model selectionModel selection is the task of selecting a model from among various candidates on the basis of performance criterion to choose the best one. In the context of learning, this may be the selection of a statistical model from a set of candidate models, given data. In the simplest cases, a pre-existing set of data is considered. However, the task can also involve the design of experiments such that the data collected is well-suited to the problem of model selection.
ConfidenceConfidence is the state of being clear-headed: either that a hypothesis or prediction is correct, or that a chosen course of action is the best or most effective. Confidence comes from the Latin word fidere which means "to trust". In contrast, arrogance or hubris is a state of unmerited confidence—belief lacking evidence and/or a reason. Overconfidence or presumptuousness is excessive belief in success without regard for potential failure.
