{"id":4678,"date":"2026-08-13T14:50:19","date_gmt":"2026-08-13T12:50:19","guid":{"rendered":"https:\/\/ellistat.com\/?post_type=guide-dutilisateur&#038;p=4678"},"modified":"2026-08-13T14:50:19","modified_gmt":"2026-08-13T12:50:19","slug":"menu-3","status":"publish","type":"guide-dutilisateur","link":"https:\/\/ellistat.com\/en\/guide-dutilisateur\/menu-3\/","title":{"rendered":"Menu"},"content":{"rendered":"<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><td><strong>Entrance<\/strong><\/td><td><strong>What is it used for?<\/strong><\/td><td><strong>Starting Point<\/strong><\/td><\/tr><\/thead><tbody><tr><td><strong>Machine Learning<\/strong><\/td><td>Fit a predictive model for a quantitative or qualitative Y based on several X variables.<\/td><td>An existing data grid.<\/td><\/tr><tr><td><strong>Optimization<\/strong><\/td><td>Find the setting that works best&nbsp;<strong>several<\/strong>&nbsp;answers at a time, based on the fitted models.<\/td><td>One or more models.<\/td><\/tr><tr><td><strong>Bayesian Optimization<\/strong><\/td><td>Achieve optimal results with a minimum number of tests: the module suggests initial tests and subsequent ones as needed.<\/td><td>Nothing\u2014factors and their limits.<\/td><\/tr><tr><td><strong>Mix Optimization<\/strong><\/td><td>The same principle applies to a formulation whose components total 100 %.<\/td><td>Nothing\u2014just the components and their terminals.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">\ud83d\udca1 The first two entries are working\u00a0<strong>based on collected data<\/strong>, the last two\u00a0<strong>lead the acquisition<\/strong>. A useful tip: If you can still decide which tests to run, sequential optimization modules will save you from having to run those tests; if the tests have already been run, that\u2019s machine learning. It is possible to incorporate previously conducted experiments into Bayesian optimization, provided that these experiments adequately cover the domain. Similarly, it is possible to start with existing mixtures, provided that these mixtures satisfy the condition \u00abSum of proportions = 1.\u00bb<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u26a0\ufe0f All four entries require a\u00a0<strong>project<\/strong>\u00a0selected. Machine Learning and Optimization also read a\u00a0<strong>grid<\/strong>\u00a0; the two sequential optimization modules are standalone and store their data within the study itself.<\/p>","protected":false},"featured_media":0,"menu_order":10,"template":"","meta":{"_acf_changed":false},"menu-guide-dutilisateur":[32],"class_list":["post-4678","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 v28.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Menu - Ellistat<\/title>\n<meta name=\"description\" content=\"Donn\u00e9es d\u00e9j\u00e0 acquises ou essais encore \u00e0 d\u00e9cider : d\u00e9couvrez les 4 entr\u00e9es du menu Machine Learning et choisissez le module adapt\u00e9 \u00e0 votre situation.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/ellistat.com\/en\/users-guide\/menu-3\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Menu - 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