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@@ -291,11 +291,11 @@ <h2 class="anchored" data-anchor-id="le-principe-fondamental-partitionner-pour-p
 <section id="les-défis-du-partitionnement-optimal" class="level3">
 <h3 class="anchored" data-anchor-id="les-défis-du-partitionnement-optimal">Les défis du partitionnement optimal</h3>
 <p>L’objectif principal est de trouver la partition de l’espace des caractéristiques qui offre les meilleures prédictions possibles. Cependant, cet objectif se heurte à plusieurs difficultés, et la complexité du problème augmente rapidement avec le nombre de caractéristiques et la taille de l’échantillon:</p>
-<ol type="1">
+<ul>
 <li><strong>Infinité des découpages possibles</strong> : Il existe une infinité de façons de diviser l’espace des caractéristiques.</li>
 <li><strong>Complexité de la paramétrisation</strong> : Il est difficile de représenter tous ces découpages avec un nombre limité de paramètres.</li>
 <li><strong>Optimisation complexe</strong> : Même avec une paramétrisation, trouver le meilleur découpage nécessite une optimisation complexe, souvent irréaliste en pratique.</li>
-</ol>
+</ul>
 </section>
 <section id="les-solutions-apportées-par-les-arbres-de-décision" class="level3">
 <h3 class="anchored" data-anchor-id="les-solutions-apportées-par-les-arbres-de-décision">Les solutions apportées par les arbres de décision</h3>
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