diff --git a/TP/TP3_mercredi/TP_Regression.ipynb b/TP/TP3_mercredi/TP_Regression.ipynb
index 069edba235c7473005f11dc868820c997d754c49..e0247e9a808d94305c9696a1305ca57a7202cd20 100644
--- a/TP/TP3_mercredi/TP_Regression.ipynb
+++ b/TP/TP3_mercredi/TP_Regression.ipynb
@@ -33,10 +33,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "import numpy as np \n",
+    "from sklearn import datasets\n",
+    "boston = datasets.load_boston()\n",
+    "X, y = boston.data, boston.target\n",
+    "feature_names = boston.feature_names"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -104,15 +110,20 @@
    "metadata": {},
    "source": [
     "## Arbre de régression\n",
-    "![](https://fr.wikipedia.org/wiki/Arbre_de_d%C3%A9cision#/media/File:Arbre_de_decision.jpg)"
+    "![](https://i0.wp.com/freakonometrics.hypotheses.org/files/2015/06/boosting-algo-3.gif?zoom=2&w=456&ssl=1)\n",
+    "\n",
+    "Les arbres de régression sont des modèles très puissants, qui divisent l'espace en zone où tout les points ont le même output. On trouvera dans scikit: https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "import matplotlib.gridspec as gridspec\n",
+    "from matplotlib import pyplot as plt"
+   ]
   },
   {
    "cell_type": "markdown",