diff --git a/notebooks/minimization_adult.ipynb b/notebooks/minimization_adult.ipynb
index bf965a6..17610a3 100644
--- a/notebooks/minimization_adult.ipynb
+++ b/notebooks/minimization_adult.ipynb
@@ -147,8 +147,10 @@
     "\n",
     "# default target_accuracy is 0.998\n",
     "minimizer = GeneralizeToRepresentative(model)\n",
-    "# Can be done either on training or test data. Doing it with test data is better as the resulting accuracy on test\n",
-    "# data will be closer to the desired target accuracy (when working with training data it could result in a larger gap)\n",
+    "\n",
+    "# Fitting the minimizar can be done either on training or test data. Doing it with test data is better as the \n",
+    "# resulting accuracy on test data will be closer to the desired target accuracy (when working with training \n",
+    "# data it could result in a larger gap)\n",
     "# Don't forget to leave a hold-out set for final validation!\n",
     "X_generalizer_train, x_test, y_generalizer_train, y_test = train_test_split(x_test, y_test, stratify=y_test,\n",
     "                                                                test_size = 0.4, random_state = 38)\n",
@@ -195,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -218,8 +220,8 @@
     }
    ],
    "source": [
-    "# We allow a 2.5% deviation in accuracy from the original model accuracy\n",
-    "minimizer2 = GeneralizeToRepresentative(model, target_accuracy=0.975)\n",
+    "# We allow a 1% deviation in accuracy from the original model accuracy\n",
+    "minimizer2 = GeneralizeToRepresentative(model, target_accuracy=0.99)\n",
     "\n",
     "minimizer2.fit(X_generalizer_train, x_train_predictions)\n",
     "transformed2 = minimizer2.transform(x_test)\n",