chore: correct accuracy display in XGBclassifier notebook

docs/advanced_examples/XGBClassifier.ipynb

@@ -26,7 +26,7 @@
     "from matplotlib.colors import ListedColormap\n",
     "from sklearn.datasets import fetch_openml, make_circles\n",
     "from sklearn.ensemble import RandomForestClassifier as SklearnRandomForestClassifier\n",
-    "from sklearn.metrics import make_scorer, matthews_corrcoef\n",
+    "from sklearn.metrics import accuracy_score, make_scorer, matthews_corrcoef\n",
     "from sklearn.model_selection import GridSearchCV, train_test_split\n",
     "from xgboost.sklearn import XGBClassifier as SklearnXGBClassifier\n",
     "\n",
@@ -611,20 +611,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Clear FP32 sklearn model MCC: 0.5\n",
-      "Clear quantized model MCC: 0.6\n",
-      "FHE model MCC: 0.6\n"
+      "Accuracy scores:\n",
+      "- Scikit-Learn (clear floating points): 0.5\n",
+      "- Concrete ML (clear quantized): 0.6\n",
+      "- Concrete ML (FHE): 0.6\n"
      ]
     }
    ],
    "source": [
-    "# Print all matthews correlation coefficients with a string to explain\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "\n",
+    "print(\"Accuracy scores:\")\n",
     "print(\n",
-    "    f\"Clear FP32 sklearn model MCC: {accuracy_score(y_test_fhe, y_preds_sklearn)}\\n\"\n",
-    "    f\"Clear quantized model MCC: {accuracy_score(y_test_fhe, y_preds_clear)}\\n\"\n",
-    "    f\"FHE model MCC: {accuracy_score(y_test_fhe, y_preds_fhe)}\"\n",
+    "    f\"- Scikit-Learn (clear floating points): {accuracy_score(y_test_fhe, y_preds_sklearn)}\\n\"\n",
+    "    f\"- Concrete ML (clear quantized): {accuracy_score(y_test_fhe, y_preds_clear)}\\n\"\n",
+    "    f\"- Concrete ML (FHE): {accuracy_score(y_test_fhe, y_preds_fhe)}\"\n",
     ")"
    ]
   }