From 1461c639abf0720f3219f0bb3f3f2f95ed37a18a Mon Sep 17 00:00:00 2001
From: SUR Frederic <frederic.sur@univ-lorraine.fr>
Date: Tue, 13 Dec 2022 14:52:39 +0000
Subject: [PATCH] Replace TP4_ex1_sujet.ipynb

---
 TP4/TP4_ex1_sujet.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/TP4/TP4_ex1_sujet.ipynb b/TP4/TP4_ex1_sujet.ipynb
index d4e1a78..79bc3ff 100755
--- a/TP4/TP4_ex1_sujet.ipynb
+++ b/TP4/TP4_ex1_sujet.ipynb
@@ -260,7 +260,7 @@
    "source": [
     "__Question 4__. Pour le noyau RBF et la valeur par défaut de $\\gamma$, la cellule suivante présente différentes classifications selon les valeurs de $C$. Retrouvez les situations identifiées dans le cas linéaire.\n",
     "\n",
-    "__Rappel__ : d'après __[la documentation](http://scikit-learn.org/stable/auto_examples/svm/plot_rbf_parameters.html)__ : _\"The C parameter trades off misclassification of training examples against simplicity of the decision surface. A low C makes the decision surface smooth, while a high C aims at classifying all training examples correctly by giving the model freedom to select more samples as support vectors.\"_ "
+    "__Rappel__ : d'après __[la documentation](http://scikit-learn.org/stable/auto_examples/svm/plot_rbf_parameters.html)__ : _\"The C parameter trades off correct classification of training examples against maximization of the decision function’s margin. For larger values of C, a smaller margin will be accepted if the decision function is better at classifying all training points correctly. A lower C will encourage a larger margin, therefore a simpler decision function, at the cost of training accuracy. In other words C behaves as a regularization parameter in the SVM.\"_ "
    ]
   },
   {
-- 
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