Generalization errorFor supervised learning applications in machine learning and statistical learning theory, generalization error (also known as the out-of-sample error or the risk) is a measure of how accurately an algorithm is able to predict outcome values for previously unseen data. Because learning algorithms are evaluated on finite samples, the evaluation of a learning algorithm may be sensitive to sampling error. As a result, measurements of prediction error on the current data may not provide much information about predictive ability on new data.
Classification en classes multiplesIn machine learning and statistical classification, multiclass classification or multinomial classification is the problem of classifying instances into one of three or more classes (classifying instances into one of two classes is called binary classification). While many classification algorithms (notably multinomial logistic regression) naturally permit the use of more than two classes, some are by nature binary algorithms; these can, however, be turned into multinomial classifiers by a variety of strategies.
Verbe modalL'auxiliaire modal ou semi-auxiliaire modal (du latin modus,-i, « mesure musicale, mode, manière ») est un des outils linguistiques parmi d'autres permettant d'exprimer une modalité, c'est-à-dire de présenter un fait comme possible, impossible, nécessaire, permis, obligatoire, souhaitable, vraisemblable. L'énoncé Il travaille est une affirmation simple, rendant compte d'un fait, alors que Il peut ou Il doit travailler sont des assertions modalisées par le recours aux verbes pouvoir et devoir.
