diff --git a/decision_tree_ensembles/tree_ensemble_example.ipynb b/decision_tree_ensembles/tree_ensemble_example.ipynb
index 50d5ede..e402448 100644
--- a/decision_tree_ensembles/tree_ensemble_example.ipynb
+++ b/decision_tree_ensembles/tree_ensemble_example.ipynb
@@ -203,29 +203,15 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "clf = DecisionTreeClassifier(random_state=0)\n",
-      "clf = RandomForestClassifier(random_state=0)\n",
-      "clf = AdaBoostClassifier(random_state=0)\n",
-      "clf = GradientBoostingClassifier(random_state=0)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "clf_list = []\n",
-    "model_name_list = ['DecisionTreeClassifier', 'RandomForestClassifier', 'AdaBoostClassifier', 'GradientBoostingClassifier']\n",
+    "model_name_list = [DecisionTreeClassifier, RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier]\n",
     "\n",
     "# train model\n",
     "for model_name in model_name_list:\n",
     "\n",
-    "    # dynamically write command to train each classifier in model_name_list\n",
-    "    cmd = 'clf = ' + model_name + '(random_state=0)'\n",
-    "    print(cmd)    # print command\n",
-    "    exec(cmd)     # execute command\n",
+    "    clf = model_name(random_state=0)\n",
     "    \n",
     "    # fit data to model\n",
     "    clf = clf.fit(X_train, y_train)\n",
@@ -241,12 +227,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "[DecisionTreeClassifier(random_state=0),\n",
-       " RandomForestClassifier(random_state=0),\n",
-       " AdaBoostClassifier(random_state=0),\n",
-       " GradientBoostingClassifier(random_state=0)]"
-      ]
+      "text/plain": "[DecisionTreeClassifier(random_state=0),\n RandomForestClassifier(random_state=0),\n AdaBoostClassifier(random_state=0),\n GradientBoostingClassifier(random_state=0)]"
      },
      "execution_count": 11,
      "metadata": {},
@@ -268,86 +249,34 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "y_pred_train = clf.predict(X_train)\n",
-      "prec_val_list.append(precision_score(y_train, y_pred_train))\n",
-      "prec_val_list.append(recall_score(y_train, y_pred_train))\n",
-      "prec_val_list.append(f1_score(y_train, y_pred_train))\n",
-      "\n",
-      "y_pred_test = clf.predict(X_test)\n",
-      "prec_val_list.append(precision_score(y_test, y_pred_test))\n",
-      "prec_val_list.append(recall_score(y_test, y_pred_test))\n",
-      "prec_val_list.append(f1_score(y_test, y_pred_test))\n",
-      "\n",
-      "y_pred_train = clf.predict(X_train)\n",
-      "prec_val_list.append(precision_score(y_train, y_pred_train))\n",
-      "prec_val_list.append(recall_score(y_train, y_pred_train))\n",
-      "prec_val_list.append(f1_score(y_train, y_pred_train))\n",
-      "\n",
-      "y_pred_test = clf.predict(X_test)\n",
-      "prec_val_list.append(precision_score(y_test, y_pred_test))\n",
-      "prec_val_list.append(recall_score(y_test, y_pred_test))\n",
-      "prec_val_list.append(f1_score(y_test, y_pred_test))\n",
-      "\n",
-      "y_pred_train = clf.predict(X_train)\n",
-      "prec_val_list.append(precision_score(y_train, y_pred_train))\n",
-      "prec_val_list.append(recall_score(y_train, y_pred_train))\n",
-      "prec_val_list.append(f1_score(y_train, y_pred_train))\n",
-      "\n",
-      "y_pred_test = clf.predict(X_test)\n",
-      "prec_val_list.append(precision_score(y_test, y_pred_test))\n",
-      "prec_val_list.append(recall_score(y_test, y_pred_test))\n",
-      "prec_val_list.append(f1_score(y_test, y_pred_test))\n",
-      "\n",
-      "y_pred_train = clf.predict(X_train)\n",
-      "prec_val_list.append(precision_score(y_train, y_pred_train))\n",
-      "prec_val_list.append(recall_score(y_train, y_pred_train))\n",
-      "prec_val_list.append(f1_score(y_train, y_pred_train))\n",
-      "\n",
-      "y_pred_test = clf.predict(X_test)\n",
-      "prec_val_list.append(precision_score(y_test, y_pred_test))\n",
-      "prec_val_list.append(recall_score(y_test, y_pred_test))\n",
-      "prec_val_list.append(f1_score(y_test, y_pred_test))\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "performance_dict = {}\n",
     "\n",
-    "dataset_name_list = ['train', 'test']\n",
-    "metric_name_list = ['precision', 'recall', 'f1']\n",
+    "dataset_list = [\n",
+    "    (X_train, y_train, \"train\"),\n",
+    "    (X_test, y_test, \"test\"),\n",
+    "]\n",
+    "metric_name_list = {precision_score: \"precision\", recall_score: \"recall\", f1_score: \"f1\"}\n",
+    "col_name_list = []\n",
     "\n",
     "for i in range(len(model_name_list)):\n",
-    "    \n",
+    "\n",
     "    clf = clf_list[i]\n",
-    "    \n",
+    "\n",
     "    prec_val_list = []\n",
     "    col_name_list = []\n",
-    "    \n",
-    "    for dataset_name in dataset_name_list:\n",
     "\n",
-    "        cmd = 'y_pred_' + dataset_name + ' = clf.predict(X_' + dataset_name + ')'\n",
-    "        print(cmd)\n",
-    "        exec(cmd)\n",
-    "        \n",
-    "        for metric_name in metric_name_list:\n",
-    "        \n",
-    "            cmd = 'prec_val_list.append(' + metric_name + '_score(y_' + dataset_name + ', y_pred_' + dataset_name + '))'\n",
-    "            print(cmd)\n",
-    "            exec(cmd)\n",
+    "    for x_dataset, y_dataset, col_name in dataset_list:\n",
+    "        y_pred = clf.predict(x_dataset)\n",
     "\n",
+    "        for metric_func, pretty_name in metric_name_list.items():\n",
+    "            prec_val_list.append(metric_func(y_dataset, y_pred))\n",
+    "            col_name_list.append(pretty_name + '_' + col_name)\n",
     "\n",
-    "            col_name_list.append(metric_name + '_' + dataset_name)\n",
-    "        print('')\n",
+    "    performance_dict[model_name_list[i].__name__] = prec_val_list\n",
     "\n",
-    "    performance_dict[model_name_list[i]] = prec_val_list\n",
-    "    \n",
-    "df_performance = np.round(pd.DataFrame.from_dict(performance_dict, orient='index', columns=col_name_list),3)"
+    "df_performance = np.round(pd.DataFrame.from_dict(performance_dict, orient='index', columns=col_name_list),3)\n"
    ]
   },
   {
@@ -357,87 +286,8 @@
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>precision_train</th>\n",
-       "      <th>recall_train</th>\n",
-       "      <th>f1_train</th>\n",
-       "      <th>precision_test</th>\n",
-       "      <th>recall_test</th>\n",
-       "      <th>f1_test</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>DecisionTreeClassifier</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.952</td>\n",
-       "      <td>0.896</td>\n",
-       "      <td>0.923</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>RandomForestClassifier</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.985</td>\n",
-       "      <td>0.955</td>\n",
-       "      <td>0.970</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>AdaBoostClassifier</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.956</td>\n",
-       "      <td>0.970</td>\n",
-       "      <td>0.963</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>GradientBoostingClassifier</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.970</td>\n",
-       "      <td>0.970</td>\n",
-       "      <td>0.970</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                            precision_train  recall_train  f1_train  \\\n",
-       "DecisionTreeClassifier                  1.0           1.0       1.0   \n",
-       "RandomForestClassifier                  1.0           1.0       1.0   \n",
-       "AdaBoostClassifier                      1.0           1.0       1.0   \n",
-       "GradientBoostingClassifier              1.0           1.0       1.0   \n",
-       "\n",
-       "                            precision_test  recall_test  f1_test  \n",
-       "DecisionTreeClassifier               0.952        0.896    0.923  \n",
-       "RandomForestClassifier               0.985        0.955    0.970  \n",
-       "AdaBoostClassifier                   0.956        0.970    0.963  \n",
-       "GradientBoostingClassifier           0.970        0.970    0.970  "
-      ]
+      "text/plain": "                            precision_train  recall_train  f1_train  \\\nDecisionTreeClassifier                  1.0           1.0       1.0   \nRandomForestClassifier                  1.0           1.0       1.0   \nAdaBoostClassifier                      1.0           1.0       1.0   \nGradientBoostingClassifier              1.0           1.0       1.0   \n\n                            precision_test  recall_test  f1_test  \nDecisionTreeClassifier               0.952        0.896    0.923  \nRandomForestClassifier               0.985        0.955    0.970  \nAdaBoostClassifier                   0.956        0.970    0.963  \nGradientBoostingClassifier           0.970        0.970    0.970  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>precision_train</th>\n      <th>recall_train</th>\n      <th>f1_train</th>\n      <th>precision_test</th>\n      <th>recall_test</th>\n      <th>f1_test</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>DecisionTreeClassifier</th>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.952</td>\n      <td>0.896</td>\n      <td>0.923</td>\n    </tr>\n    <tr>\n      <th>RandomForestClassifier</th>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.985</td>\n      <td>0.955</td>\n      <td>0.970</td>\n    </tr>\n    <tr>\n      <th>AdaBoostClassifier</th>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.956</td>\n      <td>0.970</td>\n      <td>0.963</td>\n    </tr>\n    <tr>\n      <th>GradientBoostingClassifier</th>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.970</td>\n      <td>0.970</td>\n      <td>0.970</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
      "execution_count": 13,
      "metadata": {},