{"id":233877,"date":"2024-03-21T09:29:35","date_gmt":"2024-03-21T09:29:35","guid":{"rendered":"https:\/\/datascientest.com\/?p=233877"},"modified":"2024-03-21T09:29:37","modified_gmt":"2024-03-21T09:29:37","slug":"cinq-fonctions-de-scikit-learn","status":"publish","type":"post","link":"https:\/\/datascientest.com\/cinq-fonctions-de-scikit-learn","title":{"rendered":"5 fonctions de Scikit-learn indispensables \u00e0 conna\u00eetre"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"233877\" class=\"elementor elementor-233877\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-59806af e-flex e-con-boxed e-con\" data-id=\"59806af\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f65bb27 elementor-widget elementor-widget-heading\" data-id=\"f65bb27\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">Avant de vous faire d\u00e9couvrir ses fonctions utiles, rappelons-nous ce qu\u2019est Scikit-learn et dans quel cas l\u2019utiliser. Scikit-Learn est une biblioth\u00e8que Python destin\u00e9e au Machine Learning, pour l\u2019apprentissage supervis\u00e9 ou non supervis\u00e9. Elle offre \u00e9galement la possibilit\u00e9 d'analyser des mod\u00e8les avec les moyens statistiques.<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6b53ff0 elementor-widget elementor-widget-text-editor\" data-id=\"6b53ff0\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p><span style=\"font-weight: 400;\">L\u2019installation se fait avec conda :<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7a1ff7d elementor-widget elementor-widget-text-editor\" data-id=\"7a1ff7d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<pre style=\"padding-left: 40px;\"><span style=\"font-weight: 400;\">conda install scikit-learn<\/span><\/pre>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3077726 elementor-widget elementor-widget-text-editor\" data-id=\"3077726\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Ou bien avec pip :<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-dc9309e elementor-widget elementor-widget-text-editor\" data-id=\"dc9309e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<pre style=\"padding-left: 40px;\"><span style=\"font-weight: 400;\">pip install -U scikit-learn<\/span><\/pre>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-96a2916 elementor-widget elementor-widget-text-editor\" data-id=\"96a2916\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Dans cet article, nous explorerons cinq fonctions de <a href=\"https:\/\/datascientest.com\/tout-savoir-sur-scikit-learn\">Scikit-learn<\/a> (souvent abr\u00e9g\u00e9 \u00ab\u00a0Scikit\u00a0\u00bb, ou tout simplement \u00ab\u00a0sklearn\u00a0\u00bb), qui pourraient transformer votre approche de l\u2019apprentissage des donn\u00e9es.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-cc935e4 e-flex e-con-boxed e-con\" data-id=\"cc935e4\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6683c0b elementor-widget elementor-widget-heading\" data-id=\"6683c0b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">1. Obtenez des jeux de donn\u00e9es<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c595e5b elementor-widget elementor-widget-text-editor\" data-id=\"c595e5b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">La fonction tr\u00e8s utile (surtout pour les d\u00e9butants) est la possibilit\u00e9 de travailler avec plusieurs ensembles de donn\u00e9es int\u00e9gr\u00e9s automatiquement dans Scikit-learn.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Parmi les bases les plus utilis\u00e9es, nous trouvons : l\u2019ensemble de donn\u00e9es iris, l&rsquo;ensemble de donn\u00e9es des prix des maisons, l&rsquo;ensemble de donn\u00e9es sur le diab\u00e8te. Ces ensembles de donn\u00e9es sont con\u00e7us pour \u00eatre facilement compr\u00e9hensibles, ce qui permet une mise en \u0153uvre directe de mod\u00e8les d&rsquo;apprentissage automatique.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Vous pouvez importer l&rsquo;ensemble de donn\u00e9es iris avec le code suivant :<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cfef818 elementor-widget elementor-widget-text-editor\" data-id=\"cfef818\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<pre style=\"padding-left: 40px;\"><span style=\"font-weight: 400;\">from sklearn.datasets import load_iris<\/span><\/pre>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c76b237 elementor-widget elementor-widget-text-editor\" data-id=\"c76b237\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">De mani\u00e8re similaire, vous pouvez importer d&rsquo;autres ensembles de donn\u00e9es disponibles dans la biblioth\u00e8que Scikit-learn.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-90b58c8 e-flex e-con-boxed e-con\" data-id=\"90b58c8\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-fff167b elementor-widget elementor-widget-heading\" data-id=\"fff167b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">2. Divisez vos donn\u00e9es<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a85fc72 elementor-widget elementor-widget-text-editor\" data-id=\"a85fc72\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">La premi\u00e8re \u00e9tape avant l\u2019entra\u00eenement de tout algorithme de machine learning consiste \u00e0 diviser l\u2019ensemble de donn\u00e9es en deux parties: les donn\u00e9es pour entra\u00eener le mod\u00e8le et les donn\u00e9es pour le tester (80% contre 20% respectivement dans la plupart des cas).\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Ce fractionnement est crucial pour la suite du travail, il permet de :<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Obtenir une estimation de la performance du mod\u00e8le sur des nouvelles donn\u00e9es ;<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">D\u00e9tecter l\u2019<a href=\"https:\/\/datascientest.com\/overfitting-tout-savoir\">overfitting<\/a> (surajustement &#8211; si un mod\u00e8le est ajust\u00e9 de mani\u00e8re trop pr\u00e9cise aux donn\u00e9es d&rsquo;entra\u00eenement, il peut ne pourra g\u00e9n\u00e9raliser correctement sur de nouvelles donn\u00e9es ;<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Optimiser les hyperparam\u00e8tres &#8211; en ajustant les param\u00e8tres du mod\u00e8le et en \u00e9valuant les performances sur l&rsquo;ensemble de test ;<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">V\u00e9rifier la robustesse du mod\u00e8le envers les donn\u00e9es inconnues dans des situations du monde r\u00e9el.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-dfad300 elementor-widget elementor-widget-text-editor\" data-id=\"dfad300\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">La fonction de Scikit-learn qui permet de cr\u00e9er l\u2019ensemble d&rsquo;entra\u00eenement et l\u2019ensemble de test en une ligne de code s\u2019appelle <\/span><b>train_test_split <\/b><span style=\"font-weight: 400;\">:<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b2d8872 elementor-widget elementor-widget-text-editor\" data-id=\"b2d8872\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<pre style=\"padding-left: 40px;\"><span style=\"font-weight: 400;\">from sklearn.model_selection import train_test_split<\/span><\/pre>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a6c3225 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"a6c3225\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/formation-data-analyst\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Tout apprendre des fonctions de Scikit-learn<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-30cab0e e-flex e-con-boxed e-con\" data-id=\"30cab0e\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-aa49495 elementor-widget elementor-widget-heading\" data-id=\"aa49495\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">3. Utilisez les techniques avanc\u00e9es<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-906e660 elementor-widget elementor-widget-text-editor\" data-id=\"906e660\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Il est possible d&rsquo;\u00e9lever le niveau de l&rsquo;apprentissage de donn\u00e9es en utilisant l\u2019approche qui fait appel \u00e0 l&rsquo;utilisation de plusieurs mod\u00e8les pour pr\u00e9dire la variable cible (au lieu de s&rsquo;en remettre \u00e0 un seul). L&rsquo;objectif principal de cette technique d&rsquo;ensemble est d&rsquo;att\u00e9nuer le compromis entre la variance et le biais.<\/span><\/p><p><span style=\"font-weight: 400;\">Nous divisons le jeu de donn\u00e9es de mani\u00e8re al\u00e9atoire en sous-ensembles, qui sont ensuite pr\u00e9sent\u00e9s \u00e0 diff\u00e9rents mod\u00e8les pour l&rsquo;entra\u00eenement. Lorsqu&rsquo;il est temps de faire une pr\u00e9diction, on consid\u00e8re la moyenne de toutes les pr\u00e9dictions g\u00e9n\u00e9r\u00e9es par ces mod\u00e8les.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Il en existe deux techniques principales: <a href=\"https:\/\/datascientest.com\/machine-learning-le-bagging\">bagging<\/a> (ou Bootstrap Aggregating) et <a href=\"https:\/\/datascientest.com\/algorithmes-de-boosting-adaboost-gradient-boosting-xgboost\">boosting<\/a>.<\/span><\/p><p><span style=\"font-weight: 400;\">La particularit\u00e9 du bagging r\u00e9side dans le fait que les entr\u00e9es pour diff\u00e9rents mod\u00e8les sont ind\u00e9pendantes les unes des autres, ce qui permet d&rsquo;\u00e9viter le surajustement.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">En Scikit-learn la classe <\/span><b>BaggingClassifier<\/b><span style=\"font-weight: 400;\"> peut \u00eatre utilis\u00e9e pour appliquer la technique de bagging \u00e0 diff\u00e9rents <a href=\"https:\/\/datascientest.com\/3-algorithmes-de-machine-learning-utiles-pour-votre-business\">algorithmes de machine learning<\/a> :<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6586e30 elementor-widget elementor-widget-text-editor\" data-id=\"6586e30\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<pre style=\"padding-left: 40px;\"><span style=\"font-weight: 400;\">from sklearn.ensemble import BaggingClassifier<\/span><\/pre>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-aa99bd0 elementor-widget elementor-widget-text-editor\" data-id=\"aa99bd0\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Cette technique a \u00e9t\u00e9 une source d\u2019inspiration pour la cr\u00e9ation de <a href=\"https:\/\/datascientest.com\/random-forest-definition\">Random Forest<\/a> (for\u00eat al\u00e9atoire), car un des exemples les plus courants de bagging est l&rsquo;utilisation de plusieurs arbres de d\u00e9cision pour la pr\u00e9diction.<\/span><\/p><p><span style=\"font-weight: 400;\">Boosting est une approche dans laquelle plusieurs mod\u00e8les sont form\u00e9s de mani\u00e8re \u00e0 ce que la sortie d&rsquo;un mod\u00e8le influence l&rsquo;entr\u00e9e du mod\u00e8le suivant, et une attention particuli\u00e8re est accord\u00e9e aux donn\u00e9es qui ont \u00e9t\u00e9 pr\u00e9dites de mani\u00e8re incorrecte.<\/span><\/p><p><span style=\"font-weight: 400;\">Afin de pouvoir profiter de cette technique, vous pouvez importer la classe <\/span><b>GradientBoostingClassifier<\/b><span style=\"font-weight: 400;\"> pour les probl\u00e8mes de classification :<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f10c98c elementor-widget elementor-widget-text-editor\" data-id=\"f10c98c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<pre style=\"padding-left: 40px;\"><span style=\"font-weight: 400;\">from sklearn.ensemble import GradientBoostingClassifier<\/span><\/pre>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-829b623 elementor-widget elementor-widget-text-editor\" data-id=\"829b623\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">ou <\/span><b>GradientBoostingRegressor<\/b><span style=\"font-weight: 400;\"> pour les probl\u00e8mes de r\u00e9gression :<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-aa7f42e elementor-widget elementor-widget-text-editor\" data-id=\"aa7f42e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<pre style=\"padding-left: 40px;\"><span style=\"font-weight: 400;\">from sklearn.ensemble import GradientBoostingRegressor<\/span><\/pre>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-06f649a e-flex e-con-boxed e-con\" data-id=\"06f649a\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-0ea767e elementor-widget elementor-widget-heading\" data-id=\"0ea767e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">4. \u00c9valuez la performance de votre mod\u00e8le<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5fef210 elementor-widget elementor-widget-text-editor\" data-id=\"5fef210\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Apr\u00e8s avoir appliqu\u00e9 l\u2019algorithme de machine learning, il est indispensable d\u2019\u00e9valuer la performance d&rsquo;un mod\u00e8le. La fonction pour le faire de mani\u00e8re plus robuste et fiable s\u2019appelle <\/span><b>cross_val_score<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">L&rsquo;objectif principal de cette fonction est de faire la validation crois\u00e9e. Cette approche permet de maximiser l&rsquo;utilisation des donn\u00e9es d&rsquo;entra\u00eenement en les divisant en plusieurs sous-ensembles (plis). Chacun de ces plis est utilis\u00e9 ensuite \u00e0 tour de r\u00f4le comme ensemble de validation, en laissant les autres pour d&rsquo;entra\u00eenement.<\/span><\/p><p><span style=\"font-weight: 400;\">Cela contribue \u00e0 \u00e9viter les biais li\u00e9s \u00e0 une seule division des donn\u00e9es et permet (gr\u00e2ce aux scores de performance moyens) d&rsquo;obtenir une estimation plus r\u00e9aliste de la robustesse du mod\u00e8le sur diff\u00e9rents sous-ensembles de donn\u00e9es.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Voici le code pour importer cette fonction de Scikit-learn :<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d87eb8a elementor-widget elementor-widget-text-editor\" data-id=\"d87eb8a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<pre style=\"padding-left: 40px;\"><span style=\"font-weight: 400;\">from sklearn.model_selection import cross_val_score<\/span><\/pre>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b69f154 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"b69f154\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/formation-data-analyst\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Tout savoir des  fonctions de Scikit-learn<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-cad4637 e-flex e-con-boxed e-con\" data-id=\"cad4637\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1e189cf elementor-widget elementor-widget-heading\" data-id=\"1e189cf\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">5. \u00c9purez votre code en le rendant plus \u00e9l\u00e9gant et moins consommateur<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c4d2f22 elementor-widget elementor-widget-text-editor\" data-id=\"c4d2f22\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">La fonctionnalit\u00e9 <\/span><b>Pipeline<\/b><span style=\"font-weight: 400;\"> permet de combiner plusieurs \u00e9tapes de pr\u00e9traitement et de mod\u00e9lisation en une seule s\u00e9quence.<\/span><\/p><p><span style=\"font-weight: 400;\">Cela <strong>facilite la r\u00e9p\u00e9tabilit\u00e9 du processus et l&rsquo;application coh\u00e9rente des transformations<\/strong> sur les ensembles d&rsquo;entra\u00eenement et de test.<\/span><\/p><p><span style=\"font-weight: 400;\">Voici un exemple du code dans lequel nous standardisons les donn\u00e9es, faisons <\/span><span style=\"font-weight: 400;\">la r\u00e9duction de dimension et l&rsquo;entra\u00eenement d&rsquo;un mod\u00e8le :<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cefff0a elementor-widget elementor-widget-text-editor\" data-id=\"cefff0a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<pre style=\"padding-left: 40px;\"><span style=\"font-weight: 400;\">#On import des fonctions n\u00e9cessaires<\/span><br \/><br \/><span style=\"font-weight: 400;\">from sklearn.ensemble import GradientBoostingRegressor<\/span><br \/><span style=\"font-weight: 400;\">from sklearn.model_selection import train_test_split<\/span><br \/><span style=\"font-weight: 400;\">from sklearn.preprocessing import StandardScaler<\/span><br \/><span style=\"font-weight: 400;\">from sklearn.decomposition import PCA<\/span><br \/><span style=\"font-weight: 400;\">from sklearn.metrics import mean_squared_error<\/span><br \/><br \/><span style=\"font-weight: 400;\">#On effectue le split des donn\u00e9es<\/span><br \/><br \/><span style=\"font-weight: 400;\">X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/span><br \/><br \/><span style=\"font-weight: 400;\">#On normalise les donn\u00e9es<\/span><br \/><br \/><span style=\"font-weight: 400;\">scaler = StandardScaler()<\/span><br \/><span style=\"font-weight: 400;\">X_train_scaled = scaler.fit_transform(X_train)<\/span><br \/><span style=\"font-weight: 400;\">X_test_scaled = scaler.transform(X_test)<\/span><br \/><br \/><span style=\"font-weight: 400;\">#On r\u00e9duit la dimension<\/span><br \/><br \/><span style=\"font-weight: 400;\">pca = PCA(n_components=3)<\/span><br \/><span style=\"font-weight: 400;\">X_train_pca = pca.fit_transform(X_train_scaled)<\/span><br \/><span style=\"font-weight: 400;\">X_test_pca = pca.transform(X_test_scaled)<\/span><br \/><br \/><span style=\"font-weight: 400;\">#On entra\u00eene le mod\u00e8le<\/span><br \/><br \/><span style=\"font-weight: 400;\">regressor = GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, max_depth=3, random_state=42)<\/span><br \/><span style=\"font-weight: 400;\">regressor.fit(X_train_pca, y_train)<\/span><br \/><br \/><span style=\"font-weight: 400;\">#Finalement, on \u00e9value le mod\u00e8le<\/span><br \/><br \/><span style=\"font-weight: 400;\">predictions = regressor.predict(X_test_pca)<\/span><br \/><span style=\"font-weight: 400;\">mse = mean_squared_error(y_test, predictions)<\/span><\/pre>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f3dd421 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"f3dd421\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/formation-data-analyst\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Se former aux 5 fonctions de Scikit-learn<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-335f410 elementor-widget elementor-widget-text-editor\" data-id=\"335f410\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Nous pouvons refaire le m\u00eame code en utilisant <\/span><b>Pipeline <\/b><span style=\"font-weight: 400;\">:<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1db45bd elementor-widget elementor-widget-text-editor\" data-id=\"1db45bd\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<pre style=\"padding-left: 40px;\"><span style=\"font-weight: 400;\">#On import des fonctions n\u00e9cessaires<\/span><br \/><br \/><span style=\"font-weight: 400;\">from sklearn.pipeline import Pipeline<\/span><br \/><span style=\"font-weight: 400;\">from sklearn.ensemble import GradientBoostingRegressor<\/span><br \/><span style=\"font-weight: 400;\">from sklearn.model_selection import train_test_split<\/span><br \/><span style=\"font-weight: 400;\">from sklearn.preprocessing import StandardScaler<\/span><br \/><span style=\"font-weight: 400;\">from sklearn.decomposition import PCA<\/span><br \/><span style=\"font-weight: 400;\">from sklearn.metrics import mean_squared_error<\/span><br \/><br \/><span style=\"font-weight: 400;\">#On effectue le split des donn\u00e9es<\/span><br \/><br \/><span style=\"font-weight: 400;\">X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/span><br \/><br \/><span style=\"font-weight: 400;\">#On cr\u00e9\u00e9 un pipeline<\/span><br \/><br \/><span style=\"font-weight: 400;\">pipeline = Pipeline([<\/span><br \/><span style=\"font-weight: 400;\"> \u00a0<\/span><span style=\"font-weight: 400;\">\u00a0\u00a0('scaler', StandardScaler()),<\/span><span style=\"font-weight: 400;\">\u00a0 \u00a0 \u00a0 \u00a0 #On normalise les donn\u00e9es<\/span><br \/><span style=\"font-weight: 400;\"> \u00a0<\/span><span style=\"font-weight: 400;\">\u00a0\u00a0('pca', PCA(n_components=3)),<\/span><span style=\"font-weight: 400;\">\u00a0 \u00a0 \u00a0 \u00a0 #On r\u00e9duit la dimension<\/span><br \/><span style=\"font-weight: 400;\"> \u00a0<\/span><span style=\"font-weight: 400;\">\u00a0\u00a0('regressor', GradientBoostingRegressor(n_estimators=100, \u00a0 \u00a0 learning_rate=0.1, max_depth=3, random_state=42))<\/span><span style=\"font-weight: 400;\">\u00a0\u00a0<\/span><br \/><span style=\"font-weight: 400;\">#On entra\u00eene le mod\u00e8le<\/span><br \/><span style=\"font-weight: 400;\">])<\/span><br \/><br \/><span style=\"font-weight: 400;\">#On entra\u00eene le mod\u00e8le en utilisant le pipeline<\/span><br \/><br \/><span style=\"font-weight: 400;\">pipeline.fit(X_train, y_train)<\/span><br \/><br \/><span style=\"font-weight: 400;\">#Finalement, on \u00e9value le mod\u00e8le<\/span><br \/><br \/><span style=\"font-weight: 400;\">predictions = pipeline.predict(X_test)<\/span><br \/><span style=\"font-weight: 400;\">mse = mean_squared_error(y_test, predictions)<\/span><br \/><span style=\"font-weight: 400;\">print(\"Erreur quadratique moyenne :\", mse)<\/span><\/pre>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9d867fe elementor-widget elementor-widget-text-editor\" data-id=\"9d867fe\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">L&rsquo;utilisation de<\/span><b> Pipeline<\/b><span style=\"font-weight: 400;\">, nous permet de d\u00e9finir chaque \u00e9tape avec un nom sp\u00e9cifique (ex : &lsquo;scaler&rsquo;, &lsquo;pca&rsquo;, &lsquo;regressor&rsquo;), simplifiant ainsi le processus d&rsquo;entra\u00eenement et d&rsquo;\u00e9valuation pour le mod\u00e8le.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Elle r\u00e9duit le besoin de r\u00e9p\u00e9ter plusieurs fois des \u00e9tapes similaires, ce qui am\u00e9liore la lisibilit\u00e9 du code. Dans l\u2019exemple au-dessus la version avec <\/span><b>Pipeline <\/b><span style=\"font-weight: 400;\">est plus courte d&rsquo;environ 4 lignes, ce qui repr\u00e9sente une r\u00e9duction significative, surtout \u00e0 mesure que le nombre d&rsquo;\u00e9tapes de pr\u00e9traitement et de mod\u00e9lisation augmente.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-a30141d e-flex e-con-boxed e-con\" data-id=\"a30141d\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d6c543d elementor-widget elementor-widget-heading\" data-id=\"d6c543d\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Conclusion<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f69b01c elementor-widget elementor-widget-text-editor\" data-id=\"f69b01c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Scikit-learn offre une panoplie de fonctionnalit\u00e9s puissantes susceptibles de simplifier consid\u00e9rablement le d\u00e9veloppement de vos mod\u00e8les d&rsquo;<a href=\"https:\/\/datascientest.com\/apprentissage-automatique\">apprentissage automatique<\/a>. En explorant ces <strong>cinq fonctions<\/strong> pr\u00e9sent\u00e9es, vous avez l&rsquo;opportunit\u00e9 d&rsquo;accro\u00eetre votre efficacit\u00e9 et d&rsquo;obtenir des r\u00e9sultats plus robustes. Ainsi, n&rsquo;h\u00e9sitez pas \u00e0 int\u00e9grer d\u00e8s aujourd&rsquo;hui ces fonctionnalit\u00e9s \u00e0 votre <a href=\"https:\/\/datascientest.com\/featuretools-tout-savoir\">bo\u00eete \u00e0 outils en machine learning<\/a> !<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6d473a4 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"6d473a4\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/formation-data-analyst\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Ma\u00eetriser les 5 fonctions de Scikit-learn<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-371e919 elementor-widget elementor-widget-html\" data-id=\"371e919\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\r\n{\r\n  \"@context\": \"https:\/\/schema.org\",\r\n  \"@type\": \"FAQPage\",\r\n  \"mainEntity\": [{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"Qu\u2019est-ce que Scikit-learn ?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Scikit-Learn est une biblioth\u00e8que Python destin\u00e9e au Machine Learning, pour l\u2019apprentissage supervis\u00e9 ou non supervis\u00e9. Elle offre \u00e9galement la possibilit\u00e9 d'analyser des mod\u00e8les avec les moyens statistiques.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"Que sont les ensembles de donn\u00e9es int\u00e9gr\u00e9s \u00e0 Scikit-learn ?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Parmi les ensembles de donn\u00e9es int\u00e9gr\u00e9s automatiquement dans Scikit-learn, on retrouve l\u2019ensemble de donn\u00e9es iris, l'ensemble de donn\u00e9es des prix des maisons, l'ensemble de donn\u00e9es sur le diab\u00e8te. Ces ensembles de donn\u00e9es sont con\u00e7us pour \u00eatre facilement compr\u00e9hensibles, ce qui permet une mise en \u0153uvre directe de mod\u00e8les d'apprentissage automatique.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"Pourquoi ma\u00eetriser les fonctions Scikit-learn ?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Scikit-learn offre une panoplie de fonctionnalit\u00e9s puissantes susceptibles de simplifier consid\u00e9rablement le d\u00e9veloppement de vos mod\u00e8les d'apprentissage automatique. En explorant ces cinq fonctions pr\u00e9sent\u00e9es, vous avez l'opportunit\u00e9 d'accro\u00eetre votre efficacit\u00e9 et d'obtenir des r\u00e9sultats plus robustes.\"\r\n    }\r\n  }]\r\n}\r\n<\/script>\r\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Avant de vous faire d\u00e9couvrir ses fonctions utiles, rappelons-nous ce qu\u2019est Scikit-learn et dans quel cas l\u2019utiliser. Scikit-Learn est une biblioth\u00e8que Python destin\u00e9e au Machine Learning, pour l\u2019apprentissage supervis\u00e9 ou non supervis\u00e9. Elle offre \u00e9galement la possibilit\u00e9 d&rsquo;analyser des mod\u00e8les avec les moyens statistiques. L\u2019installation se fait avec conda : conda install scikit-learn Ou bien [&hellip;]<\/p>\n","protected":false},"author":78,"featured_media":233879,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2497],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>5 fonctions de Scikit-learn indispensables \u00e0 conna\u00eetre<\/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:\/\/datascientest.com\/cinq-fonctions-de-scikit-learn\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"5 fonctions de Scikit-learn indispensables \u00e0 conna\u00eetre\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datascientest.com\/cinq-fonctions-de-scikit-learn\" \/>\n<meta property=\"og:site_name\" content=\"Formation Data Science | DataScientest.com\" \/>\n<meta property=\"article:published_time\" content=\"2024-03-21T09:29:35+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-03-21T09:29:37+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datascientest.com\/wp-content\/uploads\/2024\/03\/Illu_BLOG__postman-copie.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1024\" \/>\n\t<meta property=\"og:image:height\" content=\"562\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"J\u00e9r\u00e9my Robert\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u00c9crit par\" \/>\n\t<meta name=\"twitter:data1\" content=\"J\u00e9r\u00e9my Robert\" \/>\n\t<meta name=\"twitter:label2\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data2\" content=\"6 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/datascientest.com\/cinq-fonctions-de-scikit-learn\",\"url\":\"https:\/\/datascientest.com\/cinq-fonctions-de-scikit-learn\",\"name\":\"5 fonctions de Scikit-learn indispensables \u00e0 conna\u00eetre\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/#website\"},\"datePublished\":\"2024-03-21T09:29:35+00:00\",\"dateModified\":\"2024-03-21T09:29:37+00:00\",\"author\":{\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/7af2361c48e24e79a8aa2360e6e74925\"},\"inLanguage\":\"fr-FR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/datascientest.com\/cinq-fonctions-de-scikit-learn\"]}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/datascientest.com\/#website\",\"url\":\"https:\/\/datascientest.com\/\",\"name\":\"Formation Data Science | DataScientest.com\",\"description\":\"Leader de la formation en Data Science\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/datascientest.com\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"fr-FR\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/7af2361c48e24e79a8aa2360e6e74925\",\"name\":\"J\u00e9r\u00e9my Robert\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/11\/jeremyrobert.jpeg\",\"contentUrl\":\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/11\/jeremyrobert.jpeg\",\"caption\":\"J\u00e9r\u00e9my Robert\"},\"description\":\"J\u00e9r\u00e9my Robert est actuellement Directeur p\u00e9dagogique B2B chez DataScientest.com. 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