diff --git a/TP4/TP4_ex1_sujet.ipynb b/TP4/TP4_ex1_sujet.ipynb
index d4e1a784c3e730ca383da62682d0b2782c1c50bf..79bc3ffd440fb73f3e3d3ee43c8f212b7f2025af 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.\"_ "
    ]
   },
   {