docs: remove outdated todo

concrete-ml/svm.ipynb

@@ -93,10 +93,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "88e9b7fc",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Scikit-learn Accuracy: 0.9833\n",
+      "Concrete-ML Quantized Accuracy: 0.6833\n",
+      "Concrete-ML FHE Accuracy: 0.6833\n"
+     ]
+    }
+   ],
    "source": [
     "# Train a model with scikit-learn LinearSVC, perform prediction and compute the accuracy\n",
     "svm_sklearn = SklearnLinearSVC()\n",
@@ -158,7 +168,7 @@
     "svm_concrete = ConcreteLinearSVC()\n",
     "svm_concrete.fit(X_train, y_train)\n",
     "y_pred_concrete_clear = svm_concrete.predict(X_test)\n",
-    "accuracy_concrete_clear = accuracy_score(y_test, y_pred_sklearn)\n",
+    "accuracy_concrete_clear = accuracy_score(y_test, y_pred_concrete_clear)\n",
     "```\n",
     "\n",
     "One thing to note here is not only the model is trained using clear data, but the prediction are also performed in a *plain environment*, there is no encryption at this stage.\n",
@@ -173,7 +183,7 @@
     "circuit.client.keygen(force=False)\n",
     "# Now that a circuit is compiled, the svm_concrete can predict value with FHE\n",
     "y_pred_concrete_fhe = svm_concrete.predict(X_test, execute_in_fhe=True)\n",
-    "accuracy_concrete_fhe = accuracy_score(y_test, y_pred_sklearn)\n",
+    "accuracy_concrete_fhe = accuracy_score(y_test, y_pred_concrete_fhe)\n",
     "```\n",
     "\n",
     "Now that the model is compiled, computing predictions in with FHE is just a matter of passing a execution_in_fhe parameter set to True.\n",
@@ -398,14 +408,6 @@
     "accuracy = accuracy_score(y_test, y_pred)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "86cc8bee",
-   "metadata": {},
-   "source": [
-    "**[todo]: l'accuracy reste-t-elle la même?**"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "00a2461e",