{"id":4706,"date":"2026-08-13T14:51:40","date_gmt":"2026-08-13T12:51:40","guid":{"rendered":"https:\/\/ellistat.com\/?post_type=guide-dutilisateur&#038;p=4706"},"modified":"2026-08-13T14:51:40","modified_gmt":"2026-08-13T12:51:40","slug":"appendix-definitions-3","status":"publish","type":"guide-dutilisateur","link":"https:\/\/ellistat.com\/en\/guide-dutilisateur\/annexe-definitions-3\/","title":{"rendered":"Appendix: Definitions"},"content":{"rendered":"<ul class=\"wp-block-list\">\n<li><strong>Model<\/strong>\u00a0: a fitted function that predicts Y based on X. <\/li>\n\n\n\n<li><strong>X \/ Y<\/strong>\u00a0: the explanatory factors and the predicted response. <\/li>\n\n\n\n<li><strong>Test lines<\/strong>\u00a0: lines excluded from training, reserved for model validation. <\/li>\n\n\n\n<li><strong>Overfitting<\/strong>\u00a0: a model that reproduces its training data but fails on new data. <\/li>\n\n\n\n<li><strong>R\u00b2 \/ Adjusted R\u00b2<\/strong>\u00a0: proportion of explained variability, with the second term penalizing the number of terms. <\/li>\n\n\n\n<li><strong>VIF<\/strong>\u00a0: variance inflation factor; measures the redundancy of one term relative to the others. <\/li>\n\n\n\n<li><strong>Confusion Matrix<\/strong>\u00a0: Cross-tabulation of observed and predicted classes, based on a categorical dependent variable. <\/li>\n\n\n\n<li><strong>Desirability<\/strong>\u00a0: translation of a satisfaction rating on a scale from 0 to 1. The overall desirability is the\u00a0<strong>product<\/strong>\u00a0individual preferences. <\/li>\n\n\n\n<li><strong>Gaussian process<\/strong>\u00a0: a model that predicts a value\u00a0<strong>and<\/strong>\u00a0its uncertainty in every respect. <\/li>\n\n\n\n<li><strong>Bayesian Optimization<\/strong>\u00a0: sequential selection of tests to be performed, guided by a Gaussian process. <\/li>\n\n\n\n<li><strong>Production \/ Exploration<\/strong>\u00a0: refine around the known optimum, or explore the unknown. <\/li>\n\n\n\n<li><strong>Expected Gain (EI)<\/strong>\u00a0: Expected improvement of a proposed trial compared to the current best result. \u2013\u00a0<strong>Leave-One-Out (LOO) Cross-Validation<\/strong>\u00a0: Each test is treated as if it had not been performed. <\/li>\n\n\n\n<li><strong>Q\u00b2 (predictive R\u00b2)<\/strong>\u00a0: R\u00b2 calculated based on these leave-one-out predictions. The true measure of predictive power. \u2013\u00a0<strong>Cover 95 %<\/strong>\u00a0: percentage of tests that fall within the stated prediction range. <\/li>\n\n\n\n<li><strong>Nugget<\/strong>\u00a0: proportion of variability attributed to measurement and process noise. <\/li>\n\n\n\n<li><strong>ARD<\/strong>\u00a0: correlation lengths by factor; a short length indicates a factor to which the model makes the response react strongly. <\/li>\n\n\n\n<li><strong>Simplex<\/strong>\u00a0: the set of mixtures whose proportions add up to 1. <\/li>\n\n\n\n<li><strong>Ternary diagram<\/strong>\u00a0: Triangular representation of a three-component mixture.<\/li>\n<\/ul>","protected":false},"featured_media":0,"menu_order":70,"template":"","meta":{"_acf_changed":true},"menu-guide-dutilisateur":[32],"class_list":["post-4706","guide-dutilisateur","type-guide-dutilisateur","status-publish","hentry","menu-guide-dutilisateur-8-ml-machine-learning"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.2 - 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