diff --git a/docs/advanced_examples/DecisionTreeRegressor.ipynb b/docs/advanced_examples/DecisionTreeRegressor.ipynb
index d213c1d74..ac38543e5 100644
--- a/docs/advanced_examples/DecisionTreeRegressor.ipynb
+++ b/docs/advanced_examples/DecisionTreeRegressor.ipynb
@@ -80,9 +80,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Using ConcreteML version 1.5.0-rc0\n",
-      "With Python version 3.8.18 (default, Aug 28 2023, 08:26:46) \n",
-      "[GCC 9.4.0]\n"
+      "Using ConcreteML version 1.5.0\n",
+      "With Python version 3.10.11 (v3.10.11:7d4cc5aa85, Apr  4 2023, 19:05:19) [Clang 13.0.0 (clang-1300.0.29.30)]\n"
      ]
     }
    ],
@@ -140,7 +139,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -244,15 +243,7 @@
    "execution_count": 6,
    "id": "8069097d",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training on 5100 samples in 21.4577 seconds\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "default_model = ConcreteDecisionTreeRegressor(criterion=\"absolute_error\", n_bits=6, random_state=42)\n",
     "\n",
@@ -263,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "e286d33c",
    "metadata": {},
    "outputs": [
@@ -297,7 +288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "3b076c44",
    "metadata": {},
    "outputs": [
@@ -358,7 +349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "1e64ee0b-76af-4e6a-a1d7-31048115a6e7",
    "metadata": {},
    "outputs": [
@@ -405,7 +396,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "20431f4b",
    "metadata": {},
    "outputs": [],
@@ -423,7 +414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "07e5cffc",
    "metadata": {},
    "outputs": [],
@@ -433,7 +424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "177e073d",
    "metadata": {},
    "outputs": [
@@ -504,7 +495,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "6ca2fd2c",
    "metadata": {},
    "outputs": [
@@ -526,7 +517,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "132936f9",
    "metadata": {},
    "outputs": [
@@ -560,7 +551,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "cb045438",
    "metadata": {},
    "outputs": [],
@@ -572,7 +563,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "904f585b",
    "metadata": {},
    "outputs": [
@@ -604,7 +595,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "07fe03ba",
    "metadata": {},
    "outputs": [
@@ -644,7 +635,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "15008ad0-05d0-4ec2-91ea-c4be2efbc05b",
    "metadata": {},
    "outputs": [
@@ -792,6 +783,23 @@
  "metadata": {
   "execution": {
    "timeout": 10800
+  },
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
   }
  },
  "nbformat": 4,