De l’intuition au mod\u00e8le : qui construit vraiment les IA ? \u2014 MathsVivantes<\/title>\n<link href=\"https:\/\/fonts.googleapis.com\/css2?family=Playfair+Display:ital,wght@0,400;0,700;1,400&family=Source+Serif+4:ital,wght@0,300;0,400;0,600;1,300;1,400&display=swap\" rel=\"stylesheet\">\n<style>\n :root {\n --noir: #0f0e0b;\n --creme: #f5f0e8;\n --cuivre: #8b3a0f;\n --cuivre-clair: #d4621a;\n --cuivre-pale: #fdf3ec;\n --or: #c8a010;\n --or-clair: #f0d060;\n --vert: #2e6b3e;\n --bleu: #1a3a5c;\n --gris: #6b6560;\n --gris-clair: #e8e2d8;\n }\n\n * { margin: 0; padding: 0; box-sizing: border-box; }\n\n body {\n background-color: var(--creme);\n color: var(--noir);\n font-family: 'Source Serif 4', Georgia, serif;\n font-weight: 300;\n line-height: 1.8;\n }\n\n .hero {\n background: #1a0800;\n color: var(--creme);\n padding: 80px 40px 70px;\n position: relative;\n overflow: hidden;\n }\n .hero::before {\n content: \"? \u2192 \u0192 \u2192 \u2713\";\n position: absolute;\n top: 20px; right: 40px;\n font-size: 4.5rem;\n opacity: 0.06;\n font-family: 'Playfair Display', serif;\n letter-spacing: 0.3em;\n }\n .hero-inner { max-width: 760px; 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}\n }\n<\/style>\n<\/head>\n<body>\n\n<div class=\"hero\">\n <div class=\"hero-inner\">\n <div class=\"article-rubrique\">Data Science \u00b7 Article #12<\/div>\n <h1>De l’intuition au mod\u00e8le :<br><em>qui construit vraiment les IA ?<\/em><\/h1>\n <p class=\"hero-intro\">On croit souvent que les intelligences artificielles naissent des donn\u00e9es. Ou des algorithmes. Ou du code Python. En r\u00e9alit\u00e9, tout commence bien plus t\u00f4t \u2014 par une question simple pos\u00e9e devant un tableau noir. Voici le vrai voyage d’un mod\u00e8le, de sa naissance \u00e0 sa maturit\u00e9.<\/p>\n <div class=\"meta\">Par Odilon AKOWANOU \u00b7 MathsVivantes \u00b7 Lecture : 10 min<\/div>\n <\/div>\n<\/div>\n\n<article class=\"article-body\">\n\n <p>\n Imagine un \u00e9l\u00e8ve de 3\u00e8me \u00e0 Parakou. Depuis le d\u00e9but de l’ann\u00e9e, il travaille dur \u2014 mais ses notes stagnent. Son professeur observe quelque chose d’int\u00e9ressant : les progr\u00e8s sont rapides au d\u00e9but, puis ralentissent progressivement. Cette observation, toute simple, est le point de d\u00e9part d’un mod\u00e8le math\u00e9matique.\n <\/p>\n\n <p>\n Pas les donn\u00e9es. Pas le code. Pas l’algorithme. <strong>L’observation du r\u00e9el.<\/strong>\n <\/p>\n\n <p>\n C’est l\u00e0 la premi\u00e8re grande surprise de la data science : un mod\u00e8le ne na\u00eet pas dans un ordinateur. Il na\u00eet dans la t\u00eate de quelqu’un qui regarde le monde attentivement.\n <\/p>\n\n <h2>Qu’est-ce que mod\u00e9liser, vraiment ?<\/h2>\n\n <div class=\"definition\">\n <strong>Mod\u00e9liser :<\/strong> c’est traduire un ph\u00e9nom\u00e8ne r\u00e9el en langage math\u00e9matique. C’est construire un pont entre le monde qu’on observe et les \u00e9quations qu’on peut manipuler. Un bon mod\u00e8le n’est pas forc\u00e9ment compliqu\u00e9 \u2014 il est juste <em>juste<\/em>.\n <\/div>\n\n <p>\n Prenons trois situations que tout B\u00e9ninois peut observer autour de lui :\n <\/p>\n\n <div class=\"encadre\">\n <span class=\"encadre-titre\">\ud83c\udf0d Trois observations du quotidien africain<\/span>\n <p>\ud83c\udf93 <strong>Un \u00e9l\u00e8ve apprend vite au d\u00e9but, puis ralentit.<\/strong> \u2014 Cela ressemble \u00e0 une fonction logarithmique ou une courbe de saturation.<\/p>\n <p>\ud83c\udfe5 <strong>Un patient sous traitement peut gu\u00e9rir ou rechuter selon sa progression.<\/strong> \u2014 Cela ressemble \u00e0 un mod\u00e8le probabiliste binaire \u2014 la r\u00e9gression logistique !<\/p>\n <p>\ud83c\udf3e <strong>La production de ma\u00efs augmente avec la pluie, mais au-del\u00e0 d’un seuil, trop d’eau noie les plants.<\/strong> \u2014 Cela ressemble \u00e0 une parabole avec un maximum.<\/p>\n <\/div>\n\n <p>\n Ces trois observations sont des <strong>intuitions du r\u00e9el<\/strong>. Elles ne sont pas encore des mod\u00e8les. Mais elles en portent les germes. Le travail du mod\u00e9lisateur, c’est de les faire \u00e9clore.\n <\/p>\n\n <h2>Le voyage d’un mod\u00e8le \u2014 de l’id\u00e9e \u00e0 la pr\u00e9diction<\/h2>\n\n <div class=\"schema-container\">\n <div class=\"schema-titre\">\ud83d\uddfa\ufe0f Les 5 \u00e9tapes de la naissance d’un mod\u00e8le<\/div>\n <svg width=\"100%\" viewBox=\"0 0 560 120\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n <rect width=\"560\" height=\"120\" fill=\"white\"\/>\n\n \n <rect x=\"10\" y=\"35\" width=\"90\" height=\"50\" rx=\"3\" fill=\"#eaf7ee\" stroke=\"#2e6b3e\" stroke-width=\"1.5\"\/>\n <text x=\"55\" y=\"57\" font-size=\"10\" fill=\"#2e6b3e\" text-anchor=\"middle\" font-weight=\"bold\">Observer<\/text>\n <text x=\"55\" y=\"72\" font-size=\"9\" fill=\"#6b6560\" text-anchor=\"middle\">le ph\u00e9nom\u00e8ne<\/text>\n\n \n <line x1=\"100\" y1=\"60\" x2=\"120\" y2=\"60\" stroke=\"#d4621a\" stroke-width=\"1.5\" marker-end=\"url(#arrC)\"\/>\n <defs>\n <marker id=\"arrC\" markerWidth=\"7\" markerHeight=\"7\" refX=\"5\" refY=\"3\" orient=\"auto\">\n <path d=\"M0,0 L0,6 L7,3 z\" fill=\"#d4621a\"\/>\n <\/marker>\n <\/defs>\n\n \n <rect x=\"120\" y=\"35\" width=\"90\" height=\"50\" rx=\"3\" fill=\"#eef0ff\" stroke=\"#1a3a5c\" stroke-width=\"1.5\"\/>\n <text x=\"165\" y=\"57\" font-size=\"10\" fill=\"#1a3a5c\" text-anchor=\"middle\" font-weight=\"bold\">Formaliser<\/text>\n <text x=\"165\" y=\"72\" font-size=\"9\" fill=\"#6b6560\" text-anchor=\"middle\">en maths<\/text>\n\n \n <line x1=\"210\" y1=\"60\" x2=\"230\" y2=\"60\" stroke=\"#d4621a\" stroke-width=\"1.5\" marker-end=\"url(#arrC)\"\/>\n\n \n <rect x=\"230\" y=\"35\" width=\"90\" height=\"50\" rx=\"3\" fill=\"#fdf3ec\" stroke=\"#d4621a\" stroke-width=\"1.5\"\/>\n <text x=\"275\" y=\"57\" font-size=\"10\" fill=\"#d4621a\" text-anchor=\"middle\" font-weight=\"bold\">Estimer<\/text>\n <text x=\"275\" y=\"72\" font-size=\"9\" fill=\"#6b6560\" text-anchor=\"middle\">les param\u00e8tres<\/text>\n\n \n <line x1=\"320\" y1=\"60\" x2=\"340\" y2=\"60\" stroke=\"#d4621a\" stroke-width=\"1.5\" marker-end=\"url(#arrC)\"\/>\n\n \n <rect x=\"340\" y=\"35\" width=\"90\" height=\"50\" rx=\"3\" fill=\"#fff8e8\" stroke=\"#c8a010\" stroke-width=\"1.5\"\/>\n <text x=\"385\" y=\"57\" font-size=\"10\" fill=\"#8b5a00\" text-anchor=\"middle\" font-weight=\"bold\">Valider<\/text>\n <text x=\"385\" y=\"72\" font-size=\"9\" fill=\"#6b6560\" text-anchor=\"middle\">sur donn\u00e9es<\/text>\n\n \n <line x1=\"430\" y1=\"60\" x2=\"450\" y2=\"60\" stroke=\"#d4621a\" stroke-width=\"1.5\" marker-end=\"url(#arrC)\"\/>\n\n \n <rect x=\"450\" y=\"35\" width=\"90\" height=\"50\" rx=\"3\" fill=\"#0f0e0b\" stroke=\"#0f0e0b\" stroke-width=\"1.5\"\/>\n <text x=\"495\" y=\"57\" font-size=\"10\" fill=\"white\" text-anchor=\"middle\" font-weight=\"bold\">D\u00e9ployer<\/text>\n <text x=\"495\" y=\"72\" font-size=\"9\" fill=\"rgba(245,240,232,0.7)\" text-anchor=\"middle\">et pr\u00e9dire<\/text>\n\n \n <text x=\"55\" y=\"100\" font-size=\"8\" fill=\"#2e6b3e\" text-anchor=\"middle\">Expert domaine<\/text>\n <text x=\"165\" y=\"100\" font-size=\"8\" fill=\"#1a3a5c\" text-anchor=\"middle\">Math\u00e9maticien<\/text>\n <text x=\"275\" y=\"100\" font-size=\"8\" fill=\"#d4621a\" text-anchor=\"middle\">Data scientist<\/text>\n <text x=\"385\" y=\"100\" font-size=\"8\" fill=\"#8b5a00\" text-anchor=\"middle\">Data scientist<\/text>\n <text x=\"495\" y=\"100\" font-size=\"8\" fill=\"#6b6560\" text-anchor=\"middle\">Ing\u00e9nieur<\/text>\n <\/svg>\n <\/div>\n\n <p>\n Ce sch\u00e9ma r\u00e9v\u00e8le quelque chose de fondamental : <strong>construire un mod\u00e8le est un travail collectif<\/strong>. Personne ne peut faire les 5 \u00e9tapes seul avec la m\u00eame excellence. C’est pour \u00e7a que les meilleures \u00e9quipes de data science sont pluridisciplinaires.\n <\/p>\n\n <h2>Les trois b\u00e2tisseurs du mod\u00e8le<\/h2>\n\n <p>\n Derri\u00e8re chaque mod\u00e8le performant, on trouve g\u00e9n\u00e9ralement trois types de comp\u00e9tences qui s’articulent comme les pi\u00e8ces d’un m\u00eame puzzle.\n <\/p>\n\n <div class=\"trio\">\n <div class=\"trio-bloc\">\n <span class=\"trio-icone\">\ud83d\udd0d<\/span>\n <div class=\"trio-titre\">L’Expert du domaine<\/div>\n <div class=\"trio-desc\">Il conna\u00eet le terrain. Il sait ce qui se passe vraiment \u2014 pas dans les livres, mais dans la r\u00e9alit\u00e9. C’est lui qui pose la bonne question.<\/div>\n <span class=\"trio-tag tag-expert\">Intuition<\/span>\n <\/div>\n <div class=\"trio-bloc\">\n <span class=\"trio-icone\">\ud83d\udcd0<\/span>\n <div class=\"trio-titre\">Le Math\u00e9maticien<\/div>\n <div class=\"trio-desc\">Il traduit l’intuition en \u00e9quations. Il choisit la bonne structure math\u00e9matique, pose les hypoth\u00e8ses, garantit la coh\u00e9rence.<\/div>\n <span class=\"trio-tag tag-maths\">Formalisation<\/span>\n <\/div>\n <div class=\"trio-bloc\">\n <span class=\"trio-icone\">\ud83d\udcca<\/span>\n <div class=\"trio-titre\">Le Data Scientist<\/div>\n <div class=\"trio-desc\">Il rend le mod\u00e8le op\u00e9rationnel. Il estime les param\u00e8tres, valide sur les donn\u00e9es, mesure les performances.<\/div>\n <span class=\"trio-tag tag-stat\">Validation<\/span>\n <\/div>\n <\/div>\n\n <div class=\"encadre-histoire encadre\">\n <span class=\"encadre-titre\">\ud83c\udf0d Exemple b\u00e9ninois concret<\/span>\n <p>\n Imagine qu’on veuille mod\u00e9liser la propagation du paludisme dans les villages du Sud-B\u00e9nin pendant la saison des pluies.\n <\/p>\n <p>\n <strong>L’expert du domaine<\/strong> (un m\u00e9decin de l’EPAC ou du CNHU) observe : les cas explosent 2 semaines apr\u00e8s les grandes pluies, surtout dans les zones de marigots. Il identifie les facteurs cl\u00e9s : temp\u00e9rature, humidit\u00e9, proximit\u00e9 de l’eau.\n <\/p>\n <p>\n <strong>Le math\u00e9maticien<\/strong> formalise : il propose un mod\u00e8le SIR modifi\u00e9 (comme celui de notre article #7 !) avec un param\u00e8tre \u03b2 saisonnier qui d\u00e9pend des pr\u00e9cipitations. Il pose les \u00e9quations diff\u00e9rentielles correspondantes.\n <\/p>\n <p>\n <strong>Le data scientist<\/strong> collecte les donn\u00e9es des centres de sant\u00e9 des 5 derni\u00e8res ann\u00e9es, estime \u03b2 et \u03b3 par r\u00e9gression, valide le mod\u00e8le sur une r\u00e9gion test, et ajuste.\n <\/p>\n <p>\n R\u00e9sultat : un outil qui pr\u00e9dit les pics de paludisme 3 semaines \u00e0 l’avance \u2014 et permet d’envoyer les moustiquaires et m\u00e9dicaments au bon endroit, au bon moment.\n <\/p>\n <\/div>\n\n <h2>Le grand malentendu sur la data science<\/h2>\n\n <p>\n Beaucoup de gens \u2014 et m\u00eame beaucoup de data scientists d\u00e9butants \u2014 croient que leur travail consiste \u00e0 <em>trouver<\/em> un mod\u00e8le dans les donn\u00e9es. Ils lancent dix algorithmes diff\u00e9rents, gardent celui qui a le meilleur score, et appellent \u00e7a de la \u00ab\u00a0mod\u00e9lisation\u00a0\u00bb.\n <\/p>\n\n <p>\n C’est une erreur profonde. Et dangereuse.\n <\/p>\n\n <div class=\"encadre\">\n <span class=\"encadre-titre\">\u26a0\ufe0f Le pi\u00e8ge du \u00ab\u00a0meilleur algorithme\u00a0\u00bb<\/span>\n <p>Un mod\u00e8le qui performe bien sur les donn\u00e9es d’entra\u00eenement sans compr\u00e9hension du ph\u00e9nom\u00e8ne est comme un \u00e9tudiant qui apprend par c\u0153ur les r\u00e9ponses de l’examen pr\u00e9c\u00e9dent. Il r\u00e9ussira ce test. Il \u00e9chouera sur le prochain.<\/p>\n <p>En data science, ce probl\u00e8me s’appelle le <strong>surapprentissage (overfitting)<\/strong> \u2014 le mod\u00e8le a m\u00e9moris\u00e9 les donn\u00e9es au lieu de comprendre le ph\u00e9nom\u00e8ne.<\/p>\n <p>La mod\u00e9lisation \u2014 comprendre d’abord <em>pourquoi<\/em> un ph\u00e9nom\u00e8ne se comporte ainsi \u2014 est le meilleur antidote au surapprentissage.<\/p>\n <\/div>\n\n <p>\n Un bon data scientist ne se demande pas \u00ab\u00a0quel algorithme donne le meilleur score ?\u00a0\u00bb mais <strong>\u00ab\u00a0est-ce que mon mod\u00e8le capture quelque chose de vrai sur le monde ?\u00a0\u00bb<\/strong>\n <\/p>\n\n <h2>Les trois piliers d’un bon mod\u00e8le moderne<\/h2>\n\n <p>\n Les mod\u00e8les les plus puissants d’aujourd’hui \u2014 qu’il s’agisse de pr\u00e9dire les crues, d\u00e9tecter les maladies ou recommander des cours en ligne \u2014 reposent sur trois piliers indissociables :\n <\/p>\n\n <div class=\"piliers\">\n <div class=\"pilier p1\">\n <span class=\"pilier-num\">\u2460<\/span>\n <div class=\"pilier-titre\">L’intuition<\/div>\n <div class=\"pilier-desc\">Comprendre le ph\u00e9nom\u00e8ne r\u00e9el avant toute formule. Souvent inspir\u00e9e de la physique, de la biologie ou de l’exp\u00e9rience de terrain.<\/div>\n <\/div>\n <div class=\"pilier p2\">\n <span class=\"pilier-num\">\u2461<\/span>\n <div class=\"pilier-titre\">La formalisation<\/div>\n <div class=\"pilier-desc\">Traduire cette intuition en \u00e9quations rigoureuses. Choisir la bonne structure : lin\u00e9aire, diff\u00e9rentielle, probabiliste, neuronale…<\/div>\n <\/div>\n <div class=\"pilier p3\">\n <span class=\"pilier-num\">\u2462<\/span>\n <div class=\"pilier-titre\">Les donn\u00e9es<\/div>\n <div class=\"pilier-desc\">Calibrer, valider et affiner le mod\u00e8le sur des observations r\u00e9elles. Les donn\u00e9es ne cr\u00e9ent pas le mod\u00e8le \u2014 elles l’aff\u00fbtent.<\/div>\n <\/div>\n <\/div>\n\n <p>\n Remarque l’ordre : l’intuition vient en premier. Les donn\u00e9es viennent en dernier. Ce n’est pas pour minimiser l’importance des donn\u00e9es \u2014 elles sont essentielles \u2014 mais pour rappeler qu’elles ne peuvent pas se substituer \u00e0 la compr\u00e9hension.\n <\/p>\n\n <h2>Le profil du futur : le math\u00e9maticien-terrain<\/h2>\n\n <p>\n Les avanc\u00e9es r\u00e9centes de l’IA ont cr\u00e9\u00e9 un besoin nouveau sur le march\u00e9 africain et mondial : un profil capable de faire les trois choses \u00e0 la fois. Ni purement th\u00e9orique, ni purement technique. Un pont entre le monde r\u00e9el et le monde des \u00e9quations.\n <\/p>\n\n <blockquote>\n Le math\u00e9maticien qui ne conna\u00eet que ses \u00e9quations construit des mod\u00e8les qui ne fonctionnent pas. Le praticien qui ne conna\u00eet que son terrain construit des solutions qui ne g\u00e9n\u00e9ralisent pas. Celui qui ma\u00eetrise les deux change le monde.\n <cite>\u2014 Philosophie du data scientist africain<\/cite>\n <\/blockquote>\n\n <p>\n Ce profil hybride \u2014 math\u00e9maticien, statisticien et expert du domaine \u00e0 la fois \u2014 est pr\u00e9cis\u00e9ment ce que toi, enseignant de math\u00e9matiques qui apprend la data science, tu es en train de devenir. Et l’Afrique en a cruellement besoin.\n <\/p>\n\n <p>\n Pourquoi l’Afrique en particulier ? Parce que les mod\u00e8les import\u00e9s d’Europe ou d’Am\u00e9rique du Nord sont calibr\u00e9s sur des donn\u00e9es qui ne ressemblent pas aux r\u00e9alit\u00e9s africaines. Les distributions de revenus, les comportements de mobilit\u00e9, les patterns de maladies, les habitudes alimentaires \u2014 tout diff\u00e8re. Seul quelqu’un qui comprend \u00e0 la fois les maths <em>et<\/em> le contexte local peut construire des mod\u00e8les qui fonctionnent vraiment ici.\n <\/p>\n\n <h2>En pratique : comment mod\u00e9liser un ph\u00e9nom\u00e8ne ?<\/h2>\n\n <p>\n Voici la d\u00e9marche concr\u00e8te que tout bon mod\u00e9lisateur applique \u2014 qu’il travaille sur la sant\u00e9, l’\u00e9ducation ou l’agriculture :\n <\/p>\n\n <div class=\"encadre\">\n <span class=\"encadre-titre\">\ud83d\udee0\ufe0f D\u00e9marche de mod\u00e9lisation en 6 \u00e9tapes<\/span>\n <p><strong>1. D\u00e9finir la question.<\/strong> Pas \u00ab\u00a0comment analyser ces donn\u00e9es\u00a0\u00bb mais \u00ab\u00a0quel ph\u00e9nom\u00e8ne est-ce qu’on cherche \u00e0 comprendre ou pr\u00e9dire ?\u00a0\u00bb<\/p>\n <p><strong>2. Observer et interviewer.<\/strong> Parler aux experts du terrain. Aller voir. Comprendre les m\u00e9canismes avant de toucher au clavier.<\/p>\n <p><strong>3. Choisir une structure math\u00e9matique.<\/strong> Lin\u00e9aire ? Exponentielle ? Probabiliste ? Diff\u00e9rentielle ? Ce choix doit \u00eatre guid\u00e9 par le ph\u00e9nom\u00e8ne, pas par la mode algorithmique.<\/p>\n <p><strong>4. Collecter les bonnes donn\u00e9es.<\/strong> Pas toutes les donn\u00e9es \u2014 les donn\u00e9es pertinentes pour le mod\u00e8le choisi.<\/p>\n <p><strong>5. Estimer et valider.<\/strong> Ajuster les param\u00e8tres, tester sur des donn\u00e9es non vues, mesurer les erreurs.<\/p>\n <p><strong>6. Interpr\u00e9ter et communiquer.<\/strong> Un mod\u00e8le qui ne peut pas \u00eatre expliqu\u00e9 \u00e0 l’expert du domaine est un mod\u00e8le suspect.<\/p>\n <\/div>\n\n \n <div class=\"exercice\">\n <h3>\u270f\ufe0f Exercice de r\u00e9flexion<\/h3>\n <p>\n Pour chacun des trois ph\u00e9nom\u00e8nes suivants, r\u00e9ponds aux questions : (a) qui serait l’expert du domaine id\u00e9al ? (b) quelle structure math\u00e9matique proposes-tu ? (c) quelles donn\u00e9es collecterais-tu ?\n <\/p>\n <p><strong>Ph\u00e9nom\u00e8ne 1 :<\/strong> La vitesse \u00e0 laquelle les \u00e9l\u00e8ves b\u00e9ninois progressent en math\u00e9matiques au fil des trimestres.<\/p>\n <p><strong>Ph\u00e9nom\u00e8ne 2 :<\/strong> Le risque qu’un agriculteur du B\u00e9nin abandonne sa culture de coton pour une autre culture.<\/p>\n <p><strong>Ph\u00e9nom\u00e8ne 3 :<\/strong> L’\u00e9volution du prix du carburant \u00e0 Cotonou en fonction du taux de change et du prix du baril.<\/p>\n\n <div class=\"correction\">\n <div class=\"correction-titre\">\u25b8 \u00c9l\u00e9ments de r\u00e9ponse<\/div>\n <p><strong>Ph\u00e9nom\u00e8ne 1 \u2014 Progression des \u00e9l\u00e8ves :<\/strong><\/p>\n <p>(a) Expert : enseignant + p\u00e9dagogue + psychologue de l’\u00e9ducation<\/p>\n <p>(b) Structure : courbe de saturation (type logarithmique ou sigmo\u00efde) \u2014 les progr\u00e8s sont rapides au d\u00e9but puis ralentissent<\/p>\n <p>(c) Donn\u00e9es : notes par trimestre, heures de travail, taille de classe, formation du professeur<\/p>\n <p><strong>Ph\u00e9nom\u00e8ne 2 \u2014 Abandon du coton :<\/strong><\/p>\n <p>(a) Expert : agronome + sociologue rural + \u00e9conomiste agricole<\/p>\n <p>(b) Structure : r\u00e9gression logistique (oui\/non) ou mod\u00e8le de survie<\/p>\n <p>(c) Donn\u00e9es : revenus, prix du coton, acc\u00e8s au cr\u00e9dit, m\u00e9t\u00e9o, superficie exploit\u00e9e<\/p>\n <p><strong>Ph\u00e9nom\u00e8ne 3 \u2014 Prix du carburant :<\/strong><\/p>\n <p>(a) Expert : \u00e9conomiste + sp\u00e9cialiste en \u00e9nergie<\/p>\n <p>(b) Structure : r\u00e9gression lin\u00e9aire multiple (prix = f(taux de change, prix baril, taxes…))<\/p>\n <p>(c) Donn\u00e9es : historique des prix \u00e0 Cotonou, taux USD\/XOF, prix Brent, historique des subventions \u2705<\/p>\n <\/div>\n <\/div>\n\n <div class=\"conclusion\">\n <h2>Ce qu’on retient<\/h2>\n <p>\n Un mod\u00e8le ne na\u00eet pas des donn\u00e9es. Il na\u00eet d’une question pos\u00e9e sur le r\u00e9el, formalis\u00e9e en math\u00e9matiques, puis confront\u00e9e aux donn\u00e9es. Ces trois \u00e9tapes \u2014 observer, formaliser, valider \u2014 sont indissociables. Oublier la premi\u00e8re, c’est construire sur du sable.\n <\/p>\n <p>\n Et le profil le plus pr\u00e9cieux dans la data science africaine de demain n’est pas celui qui conna\u00eet le plus d’algorithmes \u2014 c’est celui qui comprend \u00e0 la fois les math\u00e9matiques <em>et<\/em> le terrain. Celui qui peut poser la bonne question avant d’ouvrir son ordinateur.\n <\/p>\n <p>\n Dans le prochain article, nous plongerons dans la <strong>loi normale et la courbe en cloche<\/strong> \u2014 cette distribution qui gouverne les notes de tes \u00e9l\u00e8ves, les erreurs de mesure, et presque tout ce qui varie autour d’une moyenne dans la nature.\n <\/p>\n <\/div>\n\n<\/article>\n\n<\/body>\n<\/html>\n","protected":false},"excerpt":{"rendered":"<p>De l’intuition au mod\u00e8le : qui construit vraiment les IA ? \u2014 MathsVivantes Data Science \u00b7 Article #12 De l’intuition […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[10,5],"tags":[],"class_list":["post-129","post","type-post","status-publish","format-standard","hentry","category-data-et-maths","category-data-science-pratique"],"yoast_head":"\n<title>De l'intuition au mod\u00e8le - AfrikIntelligent<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/vmi3217629.contaboserver.net\/?p=129\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"De l'intuition au mod\u00e8le - AfrikIntelligent\" \/>\n<meta property=\"og:description\" content=\"De l’intuition au mod\u00e8le : qui construit vraiment les IA ? 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}\n }\n<\/style>\n<\/head>\n<body>\n\n<div class=\"hero\">\n <div class=\"hero-inner\">\n <div class=\"article-rubrique\">Data Science \u00b7 Article #12<\/div>\n <h1>De l’intuition au mod\u00e8le :<br><em>qui construit vraiment les IA ?<\/em><\/h1>\n <p class=\"hero-intro\">On croit souvent que les intelligences artificielles naissent des donn\u00e9es. Ou des algorithmes. Ou du code Python. En r\u00e9alit\u00e9, tout commence bien plus t\u00f4t \u2014 par une question simple pos\u00e9e devant un tableau noir. Voici le vrai voyage d’un mod\u00e8le, de sa naissance \u00e0 sa maturit\u00e9.<\/p>\n <div class=\"meta\">Par Odilon AKOWANOU \u00b7 MathsVivantes \u00b7 Lecture : 10 min<\/div>\n <\/div>\n<\/div>\n\n<article class=\"article-body\">\n\n <p>\n Imagine un \u00e9l\u00e8ve de 3\u00e8me \u00e0 Parakou. Depuis le d\u00e9but de l’ann\u00e9e, il travaille dur \u2014 mais ses notes stagnent. Son professeur observe quelque chose d’int\u00e9ressant : les progr\u00e8s sont rapides au d\u00e9but, puis ralentissent progressivement. Cette observation, toute simple, est le point de d\u00e9part d’un mod\u00e8le math\u00e9matique.\n <\/p>\n\n <p>\n Pas les donn\u00e9es. Pas le code. Pas l’algorithme. <strong>L’observation du r\u00e9el.<\/strong>\n <\/p>\n\n <p>\n C’est l\u00e0 la premi\u00e8re grande surprise de la data science : un mod\u00e8le ne na\u00eet pas dans un ordinateur. Il na\u00eet dans la t\u00eate de quelqu’un qui regarde le monde attentivement.\n <\/p>\n\n <h2>Qu’est-ce que mod\u00e9liser, vraiment ?<\/h2>\n\n <div class=\"definition\">\n <strong>Mod\u00e9liser :<\/strong> c’est traduire un ph\u00e9nom\u00e8ne r\u00e9el en langage math\u00e9matique. C’est construire un pont entre le monde qu’on observe et les \u00e9quations qu’on peut manipuler. Un bon mod\u00e8le n’est pas forc\u00e9ment compliqu\u00e9 \u2014 il est juste <em>juste<\/em>.\n <\/div>\n\n <p>\n Prenons trois situations que tout B\u00e9ninois peut observer autour de lui :\n <\/p>\n\n <div class=\"encadre\">\n <span class=\"encadre-titre\">\ud83c\udf0d Trois observations du quotidien africain<\/span>\n <p>\ud83c\udf93 <strong>Un \u00e9l\u00e8ve apprend vite au d\u00e9but, puis ralentit.<\/strong> \u2014 Cela ressemble \u00e0 une fonction logarithmique ou une courbe de saturation.<\/p>\n <p>\ud83c\udfe5 <strong>Un patient sous traitement peut gu\u00e9rir ou rechuter selon sa progression.<\/strong> \u2014 Cela ressemble \u00e0 un mod\u00e8le probabiliste binaire \u2014 la r\u00e9gression logistique !<\/p>\n <p>\ud83c\udf3e <strong>La production de ma\u00efs augmente avec la pluie, mais au-del\u00e0 d’un seuil, trop d’eau noie les plants.<\/strong> \u2014 Cela ressemble \u00e0 une parabole avec un maximum.<\/p>\n <\/div>\n\n <p>\n Ces trois observations sont des <strong>intuitions du r\u00e9el<\/strong>. Elles ne sont pas encore des mod\u00e8les. Mais elles en portent les germes. Le travail du mod\u00e9lisateur, c’est de les faire \u00e9clore.\n <\/p>\n\n <h2>Le voyage d’un mod\u00e8le \u2014 de l’id\u00e9e \u00e0 la pr\u00e9diction<\/h2>\n\n <div class=\"schema-container\">\n <div class=\"schema-titre\">\ud83d\uddfa\ufe0f Les 5 \u00e9tapes de la naissance d’un mod\u00e8le<\/div>\n <svg width=\"100%\" viewBox=\"0 0 560 120\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n <rect width=\"560\" height=\"120\" fill=\"white\"\/>\n\n \n <rect x=\"10\" y=\"35\" width=\"90\" height=\"50\" rx=\"3\" fill=\"#eaf7ee\" stroke=\"#2e6b3e\" stroke-width=\"1.5\"\/>\n <text x=\"55\" y=\"57\" font-size=\"10\" fill=\"#2e6b3e\" text-anchor=\"middle\" font-weight=\"bold\">Observer<\/text>\n <text x=\"55\" y=\"72\" font-size=\"9\" fill=\"#6b6560\" text-anchor=\"middle\">le ph\u00e9nom\u00e8ne<\/text>\n\n \n <line x1=\"100\" y1=\"60\" x2=\"120\" y2=\"60\" stroke=\"#d4621a\" stroke-width=\"1.5\" marker-end=\"url(#arrC)\"\/>\n <defs>\n <marker id=\"arrC\" markerWidth=\"7\" markerHeight=\"7\" refX=\"5\" refY=\"3\" orient=\"auto\">\n <path d=\"M0,0 L0,6 L7,3 z\" fill=\"#d4621a\"\/>\n <\/marker>\n <\/defs>\n\n \n <rect x=\"120\" y=\"35\" width=\"90\" height=\"50\" rx=\"3\" fill=\"#eef0ff\" stroke=\"#1a3a5c\" stroke-width=\"1.5\"\/>\n <text x=\"165\" y=\"57\" font-size=\"10\" fill=\"#1a3a5c\" text-anchor=\"middle\" font-weight=\"bold\">Formaliser<\/text>\n <text x=\"165\" y=\"72\" font-size=\"9\" fill=\"#6b6560\" text-anchor=\"middle\">en maths<\/text>\n\n \n <line x1=\"210\" y1=\"60\" x2=\"230\" y2=\"60\" stroke=\"#d4621a\" stroke-width=\"1.5\" marker-end=\"url(#arrC)\"\/>\n\n \n <rect x=\"230\" y=\"35\" width=\"90\" height=\"50\" rx=\"3\" fill=\"#fdf3ec\" stroke=\"#d4621a\" stroke-width=\"1.5\"\/>\n <text x=\"275\" y=\"57\" font-size=\"10\" fill=\"#d4621a\" text-anchor=\"middle\" font-weight=\"bold\">Estimer<\/text>\n <text x=\"275\" y=\"72\" font-size=\"9\" fill=\"#6b6560\" text-anchor=\"middle\">les param\u00e8tres<\/text>\n\n \n <line x1=\"320\" y1=\"60\" x2=\"340\" y2=\"60\" stroke=\"#d4621a\" stroke-width=\"1.5\" marker-end=\"url(#arrC)\"\/>\n\n \n <rect x=\"340\" y=\"35\" width=\"90\" height=\"50\" rx=\"3\" fill=\"#fff8e8\" stroke=\"#c8a010\" stroke-width=\"1.5\"\/>\n <text x=\"385\" y=\"57\" font-size=\"10\" fill=\"#8b5a00\" text-anchor=\"middle\" font-weight=\"bold\">Valider<\/text>\n <text x=\"385\" y=\"72\" font-size=\"9\" fill=\"#6b6560\" text-anchor=\"middle\">sur donn\u00e9es<\/text>\n\n \n <line x1=\"430\" y1=\"60\" x2=\"450\" y2=\"60\" stroke=\"#d4621a\" stroke-width=\"1.5\" marker-end=\"url(#arrC)\"\/>\n\n \n <rect x=\"450\" y=\"35\" width=\"90\" height=\"50\" rx=\"3\" fill=\"#0f0e0b\" stroke=\"#0f0e0b\" stroke-width=\"1.5\"\/>\n <text x=\"495\" y=\"57\" font-size=\"10\" fill=\"white\" text-anchor=\"middle\" font-weight=\"bold\">D\u00e9ployer<\/text>\n <text x=\"495\" y=\"72\" font-size=\"9\" fill=\"rgba(245,240,232,0.7)\" text-anchor=\"middle\">et pr\u00e9dire<\/text>\n\n \n <text x=\"55\" y=\"100\" font-size=\"8\" fill=\"#2e6b3e\" text-anchor=\"middle\">Expert domaine<\/text>\n <text x=\"165\" y=\"100\" font-size=\"8\" fill=\"#1a3a5c\" text-anchor=\"middle\">Math\u00e9maticien<\/text>\n <text x=\"275\" y=\"100\" font-size=\"8\" fill=\"#d4621a\" text-anchor=\"middle\">Data scientist<\/text>\n <text x=\"385\" y=\"100\" font-size=\"8\" fill=\"#8b5a00\" text-anchor=\"middle\">Data scientist<\/text>\n <text x=\"495\" y=\"100\" font-size=\"8\" fill=\"#6b6560\" text-anchor=\"middle\">Ing\u00e9nieur<\/text>\n <\/svg>\n <\/div>\n\n <p>\n Ce sch\u00e9ma r\u00e9v\u00e8le quelque chose de fondamental : <strong>construire un mod\u00e8le est un travail collectif<\/strong>. Personne ne peut faire les 5 \u00e9tapes seul avec la m\u00eame excellence. C’est pour \u00e7a que les meilleures \u00e9quipes de data science sont pluridisciplinaires.\n <\/p>\n\n <h2>Les trois b\u00e2tisseurs du mod\u00e8le<\/h2>\n\n <p>\n Derri\u00e8re chaque mod\u00e8le performant, on trouve g\u00e9n\u00e9ralement trois types de comp\u00e9tences qui s’articulent comme les pi\u00e8ces d’un m\u00eame puzzle.\n <\/p>\n\n <div class=\"trio\">\n <div class=\"trio-bloc\">\n <span class=\"trio-icone\">\ud83d\udd0d<\/span>\n <div class=\"trio-titre\">L’Expert du domaine<\/div>\n <div class=\"trio-desc\">Il conna\u00eet le terrain. Il sait ce qui se passe vraiment \u2014 pas dans les livres, mais dans la r\u00e9alit\u00e9. C’est lui qui pose la bonne question.<\/div>\n <span class=\"trio-tag tag-expert\">Intuition<\/span>\n <\/div>\n <div class=\"trio-bloc\">\n <span class=\"trio-icone\">\ud83d\udcd0<\/span>\n <div class=\"trio-titre\">Le Math\u00e9maticien<\/div>\n <div class=\"trio-desc\">Il traduit l’intuition en \u00e9quations. Il choisit la bonne structure math\u00e9matique, pose les hypoth\u00e8ses, garantit la coh\u00e9rence.<\/div>\n <span class=\"trio-tag tag-maths\">Formalisation<\/span>\n <\/div>\n <div class=\"trio-bloc\">\n <span class=\"trio-icone\">\ud83d\udcca<\/span>\n <div class=\"trio-titre\">Le Data Scientist<\/div>\n <div class=\"trio-desc\">Il rend le mod\u00e8le op\u00e9rationnel. Il estime les param\u00e8tres, valide sur les donn\u00e9es, mesure les performances.<\/div>\n <span class=\"trio-tag tag-stat\">Validation<\/span>\n <\/div>\n <\/div>\n\n <div class=\"encadre-histoire encadre\">\n <span class=\"encadre-titre\">\ud83c\udf0d Exemple b\u00e9ninois concret<\/span>\n <p>\n Imagine qu’on veuille mod\u00e9liser la propagation du paludisme dans les villages du Sud-B\u00e9nin pendant la saison des pluies.\n <\/p>\n <p>\n <strong>L’expert du domaine<\/strong> (un m\u00e9decin de l’EPAC ou du CNHU) observe : les cas explosent 2 semaines apr\u00e8s les grandes pluies, surtout dans les zones de marigots. Il identifie les facteurs cl\u00e9s : temp\u00e9rature, humidit\u00e9, proximit\u00e9 de l’eau.\n <\/p>\n <p>\n <strong>Le math\u00e9maticien<\/strong> formalise : il propose un mod\u00e8le SIR modifi\u00e9 (comme celui de notre article #7 !) avec un param\u00e8tre \u03b2 saisonnier qui d\u00e9pend des pr\u00e9cipitations. Il pose les \u00e9quations diff\u00e9rentielles correspondantes.\n <\/p>\n <p>\n <strong>Le data scientist<\/strong> collecte les donn\u00e9es des centres de sant\u00e9 des 5 derni\u00e8res ann\u00e9es, estime \u03b2 et \u03b3 par r\u00e9gression, valide le mod\u00e8le sur une r\u00e9gion test, et ajuste.\n <\/p>\n <p>\n R\u00e9sultat : un outil qui pr\u00e9dit les pics de paludisme 3 semaines \u00e0 l’avance \u2014 et permet d’envoyer les moustiquaires et m\u00e9dicaments au bon endroit, au bon moment.\n <\/p>\n <\/div>\n\n <h2>Le grand malentendu sur la data science<\/h2>\n\n <p>\n Beaucoup de gens \u2014 et m\u00eame beaucoup de data scientists d\u00e9butants \u2014 croient que leur travail consiste \u00e0 <em>trouver<\/em> un mod\u00e8le dans les donn\u00e9es. Ils lancent dix algorithmes diff\u00e9rents, gardent celui qui a le meilleur score, et appellent \u00e7a de la \u00ab\u00a0mod\u00e9lisation\u00a0\u00bb.\n <\/p>\n\n <p>\n C’est une erreur profonde. Et dangereuse.\n <\/p>\n\n <div class=\"encadre\">\n <span class=\"encadre-titre\">\u26a0\ufe0f Le pi\u00e8ge du \u00ab\u00a0meilleur algorithme\u00a0\u00bb<\/span>\n <p>Un mod\u00e8le qui performe bien sur les donn\u00e9es d’entra\u00eenement sans compr\u00e9hension du ph\u00e9nom\u00e8ne est comme un \u00e9tudiant qui apprend par c\u0153ur les r\u00e9ponses de l’examen pr\u00e9c\u00e9dent. Il r\u00e9ussira ce test. Il \u00e9chouera sur le prochain.<\/p>\n <p>En data science, ce probl\u00e8me s’appelle le <strong>surapprentissage (overfitting)<\/strong> \u2014 le mod\u00e8le a m\u00e9moris\u00e9 les donn\u00e9es au lieu de comprendre le ph\u00e9nom\u00e8ne.<\/p>\n <p>La mod\u00e9lisation \u2014 comprendre d’abord <em>pourquoi<\/em> un ph\u00e9nom\u00e8ne se comporte ainsi \u2014 est le meilleur antidote au surapprentissage.<\/p>\n <\/div>\n\n <p>\n Un bon data scientist ne se demande pas \u00ab\u00a0quel algorithme donne le meilleur score ?\u00a0\u00bb mais <strong>\u00ab\u00a0est-ce que mon mod\u00e8le capture quelque chose de vrai sur le monde ?\u00a0\u00bb<\/strong>\n <\/p>\n\n <h2>Les trois piliers d’un bon mod\u00e8le moderne<\/h2>\n\n <p>\n Les mod\u00e8les les plus puissants d’aujourd’hui \u2014 qu’il s’agisse de pr\u00e9dire les crues, d\u00e9tecter les maladies ou recommander des cours en ligne \u2014 reposent sur trois piliers indissociables :\n <\/p>\n\n <div class=\"piliers\">\n <div class=\"pilier p1\">\n <span class=\"pilier-num\">\u2460<\/span>\n <div class=\"pilier-titre\">L’intuition<\/div>\n <div class=\"pilier-desc\">Comprendre le ph\u00e9nom\u00e8ne r\u00e9el avant toute formule. Souvent inspir\u00e9e de la physique, de la biologie ou de l’exp\u00e9rience de terrain.<\/div>\n <\/div>\n <div class=\"pilier p2\">\n <span class=\"pilier-num\">\u2461<\/span>\n <div class=\"pilier-titre\">La formalisation<\/div>\n <div class=\"pilier-desc\">Traduire cette intuition en \u00e9quations rigoureuses. Choisir la bonne structure : lin\u00e9aire, diff\u00e9rentielle, probabiliste, neuronale…<\/div>\n <\/div>\n <div class=\"pilier p3\">\n <span class=\"pilier-num\">\u2462<\/span>\n <div class=\"pilier-titre\">Les donn\u00e9es<\/div>\n <div class=\"pilier-desc\">Calibrer, valider et affiner le mod\u00e8le sur des observations r\u00e9elles. Les donn\u00e9es ne cr\u00e9ent pas le mod\u00e8le \u2014 elles l’aff\u00fbtent.<\/div>\n <\/div>\n <\/div>\n\n <p>\n Remarque l’ordre : l’intuition vient en premier. Les donn\u00e9es viennent en dernier. Ce n’est pas pour minimiser l’importance des donn\u00e9es \u2014 elles sont essentielles \u2014 mais pour rappeler qu’elles ne peuvent pas se substituer \u00e0 la compr\u00e9hension.\n <\/p>\n\n <h2>Le profil du futur : le math\u00e9maticien-terrain<\/h2>\n\n <p>\n Les avanc\u00e9es r\u00e9centes de l’IA ont cr\u00e9\u00e9 un besoin nouveau sur le march\u00e9 africain et mondial : un profil capable de faire les trois choses \u00e0 la fois. Ni purement th\u00e9orique, ni purement technique. Un pont entre le monde r\u00e9el et le monde des \u00e9quations.\n <\/p>\n\n <blockquote>\n Le math\u00e9maticien qui ne conna\u00eet que ses \u00e9quations construit des mod\u00e8les qui ne fonctionnent pas. Le praticien qui ne conna\u00eet que son terrain construit des solutions qui ne g\u00e9n\u00e9ralisent pas. Celui qui ma\u00eetrise les deux change le monde.\n <cite>\u2014 Philosophie du data scientist africain<\/cite>\n <\/blockquote>\n\n <p>\n Ce profil hybride \u2014 math\u00e9maticien, statisticien et expert du domaine \u00e0 la fois \u2014 est pr\u00e9cis\u00e9ment ce que toi, enseignant de math\u00e9matiques qui apprend la data science, tu es en train de devenir. Et l’Afrique en a cruellement besoin.\n <\/p>\n\n <p>\n Pourquoi l’Afrique en particulier ? Parce que les mod\u00e8les import\u00e9s d’Europe ou d’Am\u00e9rique du Nord sont calibr\u00e9s sur des donn\u00e9es qui ne ressemblent pas aux r\u00e9alit\u00e9s africaines. Les distributions de revenus, les comportements de mobilit\u00e9, les patterns de maladies, les habitudes alimentaires \u2014 tout diff\u00e8re. Seul quelqu’un qui comprend \u00e0 la fois les maths <em>et<\/em> le contexte local peut construire des mod\u00e8les qui fonctionnent vraiment ici.\n <\/p>\n\n <h2>En pratique : comment mod\u00e9liser un ph\u00e9nom\u00e8ne ?<\/h2>\n\n <p>\n Voici la d\u00e9marche concr\u00e8te que tout bon mod\u00e9lisateur applique \u2014 qu’il travaille sur la sant\u00e9, l’\u00e9ducation ou l’agriculture :\n <\/p>\n\n <div class=\"encadre\">\n <span class=\"encadre-titre\">\ud83d\udee0\ufe0f D\u00e9marche de mod\u00e9lisation en 6 \u00e9tapes<\/span>\n <p><strong>1. D\u00e9finir la question.<\/strong> Pas \u00ab\u00a0comment analyser ces donn\u00e9es\u00a0\u00bb mais \u00ab\u00a0quel ph\u00e9nom\u00e8ne est-ce qu’on cherche \u00e0 comprendre ou pr\u00e9dire ?\u00a0\u00bb<\/p>\n <p><strong>2. Observer et interviewer.<\/strong> Parler aux experts du terrain. Aller voir. Comprendre les m\u00e9canismes avant de toucher au clavier.<\/p>\n <p><strong>3. Choisir une structure math\u00e9matique.<\/strong> Lin\u00e9aire ? Exponentielle ? Probabiliste ? Diff\u00e9rentielle ? Ce choix doit \u00eatre guid\u00e9 par le ph\u00e9nom\u00e8ne, pas par la mode algorithmique.<\/p>\n <p><strong>4. Collecter les bonnes donn\u00e9es.<\/strong> Pas toutes les donn\u00e9es \u2014 les donn\u00e9es pertinentes pour le mod\u00e8le choisi.<\/p>\n <p><strong>5. Estimer et valider.<\/strong> Ajuster les param\u00e8tres, tester sur des donn\u00e9es non vues, mesurer les erreurs.<\/p>\n <p><strong>6. Interpr\u00e9ter et communiquer.<\/strong> Un mod\u00e8le qui ne peut pas \u00eatre expliqu\u00e9 \u00e0 l’expert du domaine est un mod\u00e8le suspect.<\/p>\n <\/div>\n\n \n <div class=\"exercice\">\n <h3>\u270f\ufe0f Exercice de r\u00e9flexion<\/h3>\n <p>\n Pour chacun des trois ph\u00e9nom\u00e8nes suivants, r\u00e9ponds aux questions : (a) qui serait l’expert du domaine id\u00e9al ? (b) quelle structure math\u00e9matique proposes-tu ? (c) quelles donn\u00e9es collecterais-tu ?\n <\/p>\n <p><strong>Ph\u00e9nom\u00e8ne 1 :<\/strong> La vitesse \u00e0 laquelle les \u00e9l\u00e8ves b\u00e9ninois progressent en math\u00e9matiques au fil des trimestres.<\/p>\n <p><strong>Ph\u00e9nom\u00e8ne 2 :<\/strong> Le risque qu’un agriculteur du B\u00e9nin abandonne sa culture de coton pour une autre culture.<\/p>\n <p><strong>Ph\u00e9nom\u00e8ne 3 :<\/strong> L’\u00e9volution du prix du carburant \u00e0 Cotonou en fonction du taux de change et du prix du baril.<\/p>\n\n <div class=\"correction\">\n <div class=\"correction-titre\">\u25b8 \u00c9l\u00e9ments de r\u00e9ponse<\/div>\n <p><strong>Ph\u00e9nom\u00e8ne 1 \u2014 Progression des \u00e9l\u00e8ves :<\/strong><\/p>\n <p>(a) Expert : enseignant + p\u00e9dagogue + psychologue de l’\u00e9ducation<\/p>\n <p>(b) Structure : courbe de saturation (type logarithmique ou sigmo\u00efde) \u2014 les progr\u00e8s sont rapides au d\u00e9but puis ralentissent<\/p>\n <p>(c) Donn\u00e9es : notes par trimestre, heures de travail, taille de classe, formation du professeur<\/p>\n <p><strong>Ph\u00e9nom\u00e8ne 2 \u2014 Abandon du coton :<\/strong><\/p>\n <p>(a) Expert : agronome + sociologue rural + \u00e9conomiste agricole<\/p>\n <p>(b) Structure : r\u00e9gression logistique (oui\/non) ou mod\u00e8le de survie<\/p>\n <p>(c) Donn\u00e9es : revenus, prix du coton, acc\u00e8s au cr\u00e9dit, m\u00e9t\u00e9o, superficie exploit\u00e9e<\/p>\n <p><strong>Ph\u00e9nom\u00e8ne 3 \u2014 Prix du carburant :<\/strong><\/p>\n <p>(a) Expert : \u00e9conomiste + sp\u00e9cialiste en \u00e9nergie<\/p>\n <p>(b) Structure : r\u00e9gression lin\u00e9aire multiple (prix = f(taux de change, prix baril, taxes…))<\/p>\n <p>(c) Donn\u00e9es : historique des prix \u00e0 Cotonou, taux USD\/XOF, prix Brent, historique des subventions \u2705<\/p>\n <\/div>\n <\/div>\n\n <div class=\"conclusion\">\n <h2>Ce qu’on retient<\/h2>\n <p>\n Un mod\u00e8le ne na\u00eet pas des donn\u00e9es. Il na\u00eet d’une question pos\u00e9e sur le r\u00e9el, formalis\u00e9e en math\u00e9matiques, puis confront\u00e9e aux donn\u00e9es. Ces trois \u00e9tapes \u2014 observer, formaliser, valider \u2014 sont indissociables. Oublier la premi\u00e8re, c’est construire sur du sable.\n <\/p>\n <p>\n Et le profil le plus pr\u00e9cieux dans la data science africaine de demain n’est pas celui qui conna\u00eet le plus d’algorithmes \u2014 c’est celui qui comprend \u00e0 la fois les math\u00e9matiques <em>et<\/em> le terrain. Celui qui peut poser la bonne question avant d’ouvrir son ordinateur.\n <\/p>\n <p>\n Dans le prochain article, nous plongerons dans la <strong>loi normale et la courbe en cloche<\/strong> \u2014 cette distribution qui gouverne les notes de tes \u00e9l\u00e8ves, les erreurs de mesure, et presque tout ce qui varie autour d’une moyenne dans la nature.\n <\/p>\n <\/div>\n\n<\/article>\n\n<\/body>\n<\/html>\n","protected":false},"excerpt":{"rendered":"<p>De l’intuition au mod\u00e8le : qui construit vraiment les IA ? \u2014 MathsVivantes Data Science \u00b7 Article #12 De l’intuition […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[10,5],"tags":[],"class_list":["post-129","post","type-post","status-publish","format-standard","hentry","category-data-et-maths","category-data-science-pratique"],"yoast_head":"\n<title>De l'intuition au mod\u00e8le - AfrikIntelligent<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/vmi3217629.contaboserver.net\/?p=129\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"De l'intuition au mod\u00e8le - AfrikIntelligent\" \/>\n<meta property=\"og:description\" content=\"De l’intuition au mod\u00e8le : qui construit vraiment les IA ? 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