diff --git a/ml1/2_6_Model_Tuning.ipynb b/ml1/2_6_Model_Tuning.ipynb
index f115de0..228d7c8 100644
--- a/ml1/2_6_Model_Tuning.ipynb
+++ b/ml1/2_6_Model_Tuning.ipynb
@@ -416,7 +416,7 @@
    "source": [
     "# Set the parameters by cross-validation\n",
     "\n",
-    "from sklearn.metrics import classification_report\n",
+    "from sklearn.metrics import classification_report, recall_score, precision_score, make_scorer\n",
     "\n",
     "# set of parameters to test\n",
     "tuned_parameters = [{'max_depth': np.arange(3, 10),\n",
@@ -434,8 +434,13 @@
     "    print(\"# Tuning hyper-parameters for %s\" % score)\n",
     "    print()\n",
     "\n",
+    "    if score == 'precision':\n",
+    "        scorer = make_scorer(precision_score, average='weighted', zero_division=0)\n",
+    "    elif score == 'recall':\n",
+    "        scorer = make_scorer(recall_score, average='weighted', zero_division=0)\n",
+    "    \n",
     "    # cv = the fold of the cross-validation cv, defaulted to 5\n",
-    "    gs = GridSearchCV(DecisionTreeClassifier(), tuned_parameters, cv=10, scoring='%s_weighted' % score)\n",
+    "    gs = GridSearchCV(DecisionTreeClassifier(), tuned_parameters, cv=10, scoring=scorer)\n",
     "    gs.fit(x_train, y_train)\n",
     "\n",
     "    print(\"Best parameters set found on development set:\")\n",
@@ -552,7 +557,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+   "version": "3.8.6"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,