diff --git a/07_ensemble_learning_and_random_forests.ipynb b/07_ensemble_learning_and_random_forests.ipynb
index 089f502..0aabfed 100644
--- a/07_ensemble_learning_and_random_forests.ipynb
+++ b/07_ensemble_learning_and_random_forests.ipynb
@@ -242,7 +242,7 @@
     "from sklearn.tree import DecisionTreeClassifier\n",
     "\n",
     "bag_clf = BaggingClassifier(\n",
-    "    DecisionTreeClassifier(random_state=42), n_estimators=500,\n",
+    "    DecisionTreeClassifier(), n_estimators=500,\n",
     "    max_samples=100, bootstrap=True, random_state=42)\n",
     "bag_clf.fit(X_train, y_train)\n",
     "y_pred = bag_clf.predict(X_test)"
@@ -327,9 +327,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from math import ceil, sqrt\n",
+    "\n",
     "bag_clf = BaggingClassifier(\n",
-    "    DecisionTreeClassifier(splitter=\"random\", max_leaf_nodes=16, random_state=42),\n",
-    "    n_estimators=500, max_samples=1.0, bootstrap=True, random_state=42)"
+    "    DecisionTreeClassifier(max_leaf_nodes=16),\n",
+    "    n_estimators=500, max_features=ceil(sqrt(X_train.shape[1])), random_state=42)"
    ]
   },
   {
@@ -362,7 +364,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "np.sum(y_pred == y_pred_rf) / len(y_pred)  # almost identical predictions"
+    "np.sum(y_pred == y_pred_rf) / len(y_pred)  # very similar predictions"
    ]
   },
   {
@@ -419,7 +421,7 @@
    "outputs": [],
    "source": [
     "bag_clf = BaggingClassifier(\n",
-    "    DecisionTreeClassifier(random_state=42), n_estimators=500,\n",
+    "    DecisionTreeClassifier(), n_estimators=500,\n",
     "    bootstrap=True, oob_score=True, random_state=40)\n",
     "bag_clf.fit(X_train, y_train)\n",
     "bag_clf.oob_score_"