diff --git a/07_ensemble_learning_and_random_forests.ipynb b/07_ensemble_learning_and_random_forests.ipynb
index d0b3ffa..432ee08 100644
--- a/07_ensemble_learning_and_random_forests.ipynb
+++ b/07_ensemble_learning_and_random_forests.ipynb
@@ -10,12 +10,12 @@
      "output_type": "stream",
      "text": [
       "CPython 3.5.5\n",
-      "IPython 6.2.1\n",
+      "IPython 6.3.1\n",
       "\n",
-      "numpy 1.14.1\n",
-      "scipy 1.0.0\n",
+      "numpy 1.14.3\n",
+      "scipy 1.0.1\n",
       "sklearn 0.19.1\n",
-      "pandas 0.22.0\n",
+      "pandas 0.23.0\n",
       "matplotlib 2.2.2\n"
      ]
     }
@@ -196,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -209,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -221,14 +221,6 @@
       "SVC 0.888\n",
       "VotingClassifier 0.896\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/haesun/anaconda3/envs/handson-ml/lib/python3.5/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
-      "  if diff:\n"
-     ]
     }
    ],
    "source": [
@@ -242,7 +234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -257,7 +249,7 @@
        "         flatten_transform=None, n_jobs=1, voting='soft', weights=None)"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -275,7 +267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -287,14 +279,6 @@
       "SVC 0.888\n",
       "VotingClassifier 0.912\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/haesun/anaconda3/envs/handson-ml/lib/python3.5/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
-      "  if diff:\n"
-     ]
     }
    ],
    "source": [
@@ -315,7 +299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -331,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -349,7 +333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -369,7 +353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -395,7 +379,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -430,7 +414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -441,7 +425,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -451,7 +435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -465,7 +449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -474,7 +458,7 @@
        "0.976"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -485,7 +469,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -510,7 +494,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -519,7 +503,7 @@
        "array([0.11249225, 0.02311929, 0.44103046, 0.423358  ])"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -530,7 +514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -565,7 +549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -574,7 +558,7 @@
        "0.9013333333333333"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -589,7 +573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -972,7 +956,7 @@
        "       [0.57291667, 0.42708333]])"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -983,7 +967,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -992,7 +976,7 @@
        "0.912"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1012,7 +996,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1022,7 +1006,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -1036,7 +1020,7 @@
        "            oob_score=False, random_state=42, verbose=0, warm_start=False)"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1048,7 +1032,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1061,7 +1045,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -1094,7 +1078,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -1110,7 +1094,7 @@
        "          learning_rate=0.5, n_estimators=200, random_state=42)"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1126,7 +1110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -1146,7 +1130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -1187,7 +1171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -1202,7 +1186,7 @@
        " 'n_classes_']"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1220,7 +1204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1229,33 +1213,6 @@
     "y = 3*X[:, 0]**2 + 0.05 * np.random.randn(100)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DecisionTreeRegressor(criterion='mse', max_depth=2, max_features=None,\n",
-       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
-       "           min_impurity_split=None, min_samples_leaf=1,\n",
-       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
-       "           presort=False, random_state=42, splitter='best')"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.tree import DecisionTreeRegressor\n",
-    "\n",
-    "tree_reg1 = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
-    "tree_reg1.fit(X, y)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 35,
@@ -1277,9 +1234,10 @@
     }
    ],
    "source": [
-    "y2 = y - tree_reg1.predict(X)\n",
-    "tree_reg2 = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
-    "tree_reg2.fit(X, y2)"
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "\n",
+    "tree_reg1 = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
+    "tree_reg1.fit(X, y)"
    ]
   },
   {
@@ -1303,18 +1261,35 @@
     }
    ],
    "source": [
-    "y3 = y2 - tree_reg2.predict(X)\n",
-    "tree_reg3 = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
-    "tree_reg3.fit(X, y3)"
+    "y2 = y - tree_reg1.predict(X)\n",
+    "tree_reg2 = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
+    "tree_reg2.fit(X, y2)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 37,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DecisionTreeRegressor(criterion='mse', max_depth=2, max_features=None,\n",
+       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
+       "           min_impurity_split=None, min_samples_leaf=1,\n",
+       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
+       "           presort=False, random_state=42, splitter='best')"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "X_new = np.array([[0.8]])"
+    "y3 = y2 - tree_reg2.predict(X)\n",
+    "tree_reg3 = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
+    "tree_reg3.fit(X, y3)"
    ]
   },
   {
@@ -1323,13 +1298,22 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "y_pred = sum(tree.predict(X_new) for tree in (tree_reg1, tree_reg2, tree_reg3))"
+    "X_new = np.array([[0.8]])"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 39,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = sum(tree.predict(X_new) for tree in (tree_reg1, tree_reg2, tree_reg3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1337,7 +1321,7 @@
        "array([0.75026781])"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1348,7 +1332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -1408,7 +1392,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -1423,7 +1407,7 @@
        "             subsample=1.0, verbose=0, warm_start=False)"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1437,7 +1421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -1452,7 +1436,7 @@
        "             subsample=1.0, verbose=0, warm_start=False)"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1464,7 +1448,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 44,
    "metadata": {
     "scrolled": true
    },
@@ -1504,7 +1488,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -1519,7 +1503,7 @@
        "             subsample=1.0, verbose=0, warm_start=False)"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1544,7 +1528,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1553,7 +1537,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -1590,7 +1574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1614,7 +1598,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -1631,9 +1615,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 50,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "최소 검증 MSE: 0.002712853325235463\n"
+     ]
+    }
+   ],
    "source": [
     "print(\"최소 검증 MSE:\", min_val_error)"
    ]
@@ -1647,7 +1639,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1660,9 +1652,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 52,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "검증 MSE: 0.0028512559726563943\n"
+     ]
+    }
+   ],
    "source": [
     "if xgboost is not None:  # 책에는 없음\n",
     "    xgb_reg = xgboost.XGBRegressor(random_state=42)\n",
@@ -1674,9 +1674,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 53,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0]\tvalidation_0-rmse:0.286719\n",
+      "Will train until validation_0-rmse hasn't improved in 2 rounds.\n",
+      "[1]\tvalidation_0-rmse:0.258221\n",
+      "[2]\tvalidation_0-rmse:0.232634\n",
+      "[3]\tvalidation_0-rmse:0.210526\n",
+      "[4]\tvalidation_0-rmse:0.190232\n",
+      "[5]\tvalidation_0-rmse:0.172196\n",
+      "[6]\tvalidation_0-rmse:0.156394\n",
+      "[7]\tvalidation_0-rmse:0.142241\n",
+      "[8]\tvalidation_0-rmse:0.129789\n",
+      "[9]\tvalidation_0-rmse:0.118752\n",
+      "[10]\tvalidation_0-rmse:0.108388\n",
+      "[11]\tvalidation_0-rmse:0.100155\n",
+      "[12]\tvalidation_0-rmse:0.09208\n",
+      "[13]\tvalidation_0-rmse:0.084791\n",
+      "[14]\tvalidation_0-rmse:0.078699\n",
+      "[15]\tvalidation_0-rmse:0.073248\n",
+      "[16]\tvalidation_0-rmse:0.069391\n",
+      "[17]\tvalidation_0-rmse:0.066277\n",
+      "[18]\tvalidation_0-rmse:0.063458\n",
+      "[19]\tvalidation_0-rmse:0.060326\n",
+      "[20]\tvalidation_0-rmse:0.0578\n",
+      "[21]\tvalidation_0-rmse:0.055643\n",
+      "[22]\tvalidation_0-rmse:0.053943\n",
+      "[23]\tvalidation_0-rmse:0.053138\n",
+      "[24]\tvalidation_0-rmse:0.052415\n",
+      "[25]\tvalidation_0-rmse:0.051821\n",
+      "[26]\tvalidation_0-rmse:0.051226\n",
+      "[27]\tvalidation_0-rmse:0.051135\n",
+      "[28]\tvalidation_0-rmse:0.05091\n",
+      "[29]\tvalidation_0-rmse:0.050893\n",
+      "[30]\tvalidation_0-rmse:0.050725\n",
+      "[31]\tvalidation_0-rmse:0.050471\n",
+      "[32]\tvalidation_0-rmse:0.050285\n",
+      "[33]\tvalidation_0-rmse:0.050492\n",
+      "[34]\tvalidation_0-rmse:0.050348\n",
+      "Stopping. Best iteration:\n",
+      "[32]\tvalidation_0-rmse:0.050285\n",
+      "\n",
+      "검증 MSE: 0.0025349167568108864\n"
+     ]
+    }
+   ],
    "source": [
     "if xgboost is not None:  # 책에는 없음\n",
     "    xgb_reg.fit(X_train, y_train,\n",
@@ -1688,18 +1735,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 54,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.3 ms ± 452 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    }
+   ],
    "source": [
     "%timeit xgboost.XGBRegressor().fit(X_train, y_train) if xgboost is not None else None"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 55,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9.82 ms ± 22.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    }
+   ],
    "source": [
     "%timeit GradientBoostingRegressor().fit(X_train, y_train)"
    ]
@@ -1733,7 +1796,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1742,7 +1805,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1751,7 +1814,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1760,7 +1823,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1779,7 +1842,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1790,7 +1853,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1802,9 +1865,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 62,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "훈련 예측기:  RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
+      "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
+      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
+      "            min_samples_leaf=1, min_samples_split=2,\n",
+      "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
+      "            oob_score=False, random_state=42, verbose=0, warm_start=False)\n",
+      "훈련 예측기:  ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',\n",
+      "           max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
+      "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
+      "           min_samples_leaf=1, min_samples_split=2,\n",
+      "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
+      "           oob_score=False, random_state=42, verbose=0, warm_start=False)\n",
+      "훈련 예측기:  LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
+      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
+      "     multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\n",
+      "     verbose=0)\n",
+      "훈련 예측기:  MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
+      "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
+      "       hidden_layer_sizes=(100,), learning_rate='constant',\n",
+      "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
+      "       nesterovs_momentum=True, power_t=0.5, random_state=42, shuffle=True,\n",
+      "       solver='adam', tol=0.0001, validation_fraction=0.1, verbose=False,\n",
+      "       warm_start=False)\n"
+     ]
+    }
+   ],
    "source": [
     "estimators = [random_forest_clf, extra_trees_clf, svm_clf, mlp_clf]\n",
     "for estimator in estimators:\n",
@@ -1814,9 +1907,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 63,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.9467, 0.9512, 0.8661, 0.9588]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "[estimator.score(X_val, y_val) for estimator in estimators]"
    ]
@@ -1837,7 +1941,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1846,7 +1950,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1860,7 +1964,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1869,27 +1973,66 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 67,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "VotingClassifier(estimators=[('random_forest_clf', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
+       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
+       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
+       "            min_samples_leaf=1, min_samples_split=2,\n",
+       "   ...       solver='adam', tol=0.0001, validation_fraction=0.1, verbose=False,\n",
+       "       warm_start=False))],\n",
+       "         flatten_transform=None, n_jobs=1, voting='hard', weights=None)"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "voting_clf.fit(X_train, y_train)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 68,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.961"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "voting_clf.score(X_val, y_val)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 69,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.9467, 0.9512, 0.8661, 0.9588]"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "[estimator.score(X_val, y_val) for estimator in voting_clf.estimators_]"
    ]
@@ -1903,9 +2046,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 70,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "VotingClassifier(estimators=[('random_forest_clf', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
+       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
+       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
+       "            min_samples_leaf=1, min_samples_split=2,\n",
+       "   ...       solver='adam', tol=0.0001, validation_fraction=0.1, verbose=False,\n",
+       "       warm_start=False))],\n",
+       "         flatten_transform=None, n_jobs=1, voting='hard', weights=None)"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "voting_clf.set_params(svm_clf=None)"
    ]
@@ -1919,9 +2079,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 71,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('random_forest_clf',\n",
+       "  RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
+       "              max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
+       "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
+       "              min_samples_leaf=1, min_samples_split=2,\n",
+       "              min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
+       "              oob_score=False, random_state=42, verbose=0, warm_start=False)),\n",
+       " ('extra_trees_clf',\n",
+       "  ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',\n",
+       "             max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
+       "             min_impurity_decrease=0.0, min_impurity_split=None,\n",
+       "             min_samples_leaf=1, min_samples_split=2,\n",
+       "             min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
+       "             oob_score=False, random_state=42, verbose=0, warm_start=False)),\n",
+       " ('svm_clf', None),\n",
+       " ('mlp_clf',\n",
+       "  MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
+       "         beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
+       "         hidden_layer_sizes=(100,), learning_rate='constant',\n",
+       "         learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
+       "         nesterovs_momentum=True, power_t=0.5, random_state=42, shuffle=True,\n",
+       "         solver='adam', tol=0.0001, validation_fraction=0.1, verbose=False,\n",
+       "         warm_start=False))]"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "voting_clf.estimators"
    ]
@@ -1935,9 +2128,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 72,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
+       "             max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
+       "             min_impurity_decrease=0.0, min_impurity_split=None,\n",
+       "             min_samples_leaf=1, min_samples_split=2,\n",
+       "             min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
+       "             oob_score=False, random_state=42, verbose=0, warm_start=False),\n",
+       " ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',\n",
+       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
+       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
+       "            min_samples_leaf=1, min_samples_split=2,\n",
+       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
+       "            oob_score=False, random_state=42, verbose=0, warm_start=False),\n",
+       " LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
+       "      intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
+       "      multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\n",
+       "      verbose=0),\n",
+       " MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
+       "        beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
+       "        hidden_layer_sizes=(100,), learning_rate='constant',\n",
+       "        learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
+       "        nesterovs_momentum=True, power_t=0.5, random_state=42, shuffle=True,\n",
+       "        solver='adam', tol=0.0001, validation_fraction=0.1, verbose=False,\n",
+       "        warm_start=False)]"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "voting_clf.estimators_"
    ]
@@ -1951,7 +2177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1967,9 +2193,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 74,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9653"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "voting_clf.score(X_val, y_val)"
    ]
@@ -1983,7 +2220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1992,9 +2229,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 76,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9703"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "voting_clf.score(X_val, y_val)"
    ]
@@ -2015,18 +2263,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 77,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9652"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "voting_clf.score(X_test, y_test)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 78,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.9434, 0.9444, 0.9541]"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "[estimator.score(X_test, y_test) for estimator in voting_clf.estimators_]"
    ]
@@ -2054,7 +2324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2066,18 +2336,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 80,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2., 2., 2., 2.],\n",
+       "       [7., 7., 7., 7.],\n",
+       "       [4., 4., 4., 4.],\n",
+       "       ...,\n",
+       "       [4., 4., 4., 4.],\n",
+       "       [9., 9., 9., 9.],\n",
+       "       [4., 4., 4., 4.]], dtype=float32)"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "X_val_predictions"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 81,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
+       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
+       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
+       "            min_samples_leaf=1, min_samples_split=2,\n",
+       "            min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=1,\n",
+       "            oob_score=True, random_state=42, verbose=0, warm_start=False)"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "rnd_forest_blender = RandomForestClassifier(n_estimators=200, oob_score=True, random_state=42)\n",
     "rnd_forest_blender.fit(X_val_predictions, y_val)"
@@ -2085,9 +2388,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 82,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9644"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "rnd_forest_blender.oob_score_"
    ]
@@ -2108,7 +2422,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2120,7 +2434,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2129,7 +2443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2138,9 +2452,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 86,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9566"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "accuracy_score(y_test, y_pred)"
    ]