diff --git a/06_decision_trees.ipynb b/06_decision_trees.ipynb
index 15fef59..ca5962f 100644
--- a/06_decision_trees.ipynb
+++ b/06_decision_trees.ipynb
@@ -249,13 +249,14 @@
     "deep_tree_clf1.fit(Xm, ym)\n",
     "deep_tree_clf2.fit(Xm, ym)\n",
     "\n",
-    "plt.figure(figsize=(11, 4))\n",
-    "plt.subplot(121)\n",
-    "plot_decision_boundary(deep_tree_clf1, Xm, ym, axes=[-1.5, 2.5, -1, 1.5], iris=False)\n",
+    "fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
+    "plt.sca(axes[0])\n",
+    "plot_decision_boundary(deep_tree_clf1, Xm, ym, axes=[-1.5, 2.4, -1, 1.5], iris=False)\n",
     "plt.title(\"No restrictions\", fontsize=16)\n",
-    "plt.subplot(122)\n",
-    "plot_decision_boundary(deep_tree_clf2, Xm, ym, axes=[-1.5, 2.5, -1, 1.5], iris=False)\n",
+    "plt.sca(axes[1])\n",
+    "plot_decision_boundary(deep_tree_clf2, Xm, ym, axes=[-1.5, 2.4, -1, 1.5], iris=False)\n",
     "plt.title(\"min_samples_leaf = {}\".format(deep_tree_clf2.min_samples_leaf), fontsize=14)\n",
+    "plt.ylabel(\"\")\n",
     "\n",
     "save_fig(\"min_samples_leaf_plot\")\n",
     "plt.show()"
@@ -299,11 +300,12 @@
     "tree_clf_sr = DecisionTreeClassifier(random_state=42)\n",
     "tree_clf_sr.fit(Xsr, ys)\n",
     "\n",
-    "plt.figure(figsize=(11, 4))\n",
-    "plt.subplot(121)\n",
+    "fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
+    "plt.sca(axes[0])\n",
     "plot_decision_boundary(tree_clf_s, Xs, ys, axes=[-0.7, 0.7, -0.7, 0.7], iris=False)\n",
-    "plt.subplot(122)\n",
+    "plt.sca(axes[1])\n",
     "plot_decision_boundary(tree_clf_sr, Xsr, ys, axes=[-0.7, 0.7, -0.7, 0.7], iris=False)\n",
+    "plt.ylabel(\"\")\n",
     "\n",
     "save_fig(\"sensitivity_to_rotation_plot\")\n",
     "plt.show()"
@@ -365,8 +367,8 @@
     "    plt.plot(X, y, \"b.\")\n",
     "    plt.plot(x1, y_pred, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
     "\n",
-    "plt.figure(figsize=(11, 4))\n",
-    "plt.subplot(121)\n",
+    "fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
+    "plt.sca(axes[0])\n",
     "plot_regression_predictions(tree_reg1, X, y)\n",
     "for split, style in ((0.1973, \"k-\"), (0.0917, \"k--\"), (0.7718, \"k--\")):\n",
     "    plt.plot([split, split], [-0.2, 1], style, linewidth=2)\n",
@@ -376,7 +378,7 @@
     "plt.legend(loc=\"upper center\", fontsize=18)\n",
     "plt.title(\"max_depth=2\", fontsize=14)\n",
     "\n",
-    "plt.subplot(122)\n",
+    "plt.sca(axes[1])\n",
     "plot_regression_predictions(tree_reg2, X, y, ylabel=None)\n",
     "for split, style in ((0.1973, \"k-\"), (0.0917, \"k--\"), (0.7718, \"k--\")):\n",
     "    plt.plot([split, split], [-0.2, 1], style, linewidth=2)\n",
@@ -428,9 +430,9 @@
     "y_pred1 = tree_reg1.predict(x1)\n",
     "y_pred2 = tree_reg2.predict(x1)\n",
     "\n",
-    "plt.figure(figsize=(11, 4))\n",
+    "fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
     "\n",
-    "plt.subplot(121)\n",
+    "plt.sca(axes[0])\n",
     "plt.plot(X, y, \"b.\")\n",
     "plt.plot(x1, y_pred1, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
     "plt.axis([0, 1, -0.2, 1.1])\n",
@@ -439,7 +441,7 @@
     "plt.legend(loc=\"upper center\", fontsize=18)\n",
     "plt.title(\"No restrictions\", fontsize=14)\n",
     "\n",
-    "plt.subplot(122)\n",
+    "plt.sca(axes[1])\n",
     "plt.plot(X, y, \"b.\")\n",
     "plt.plot(x1, y_pred2, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
     "plt.axis([0, 1, -0.2, 1.1])\n",
@@ -720,7 +722,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.7.4"
   },
   "nav_menu": {
    "height": "309px",
diff --git a/07_ensemble_learning_and_random_forests.ipynb b/07_ensemble_learning_and_random_forests.ipynb
index 432e422..19e2584 100644
--- a/07_ensemble_learning_and_random_forests.ipynb
+++ b/07_ensemble_learning_and_random_forests.ipynb
@@ -260,7 +260,7 @@
    "source": [
     "from matplotlib.colors import ListedColormap\n",
     "\n",
-    "def plot_decision_boundary(clf, X, y, axes=[-1.5, 2.5, -1, 1.5], alpha=0.5, contour=True):\n",
+    "def plot_decision_boundary(clf, X, y, axes=[-1.5, 2.45, -1, 1.5], alpha=0.5, contour=True):\n",
     "    x1s = np.linspace(axes[0], axes[1], 100)\n",
     "    x2s = np.linspace(axes[2], axes[3], 100)\n",
     "    x1, x2 = np.meshgrid(x1s, x2s)\n",
@@ -284,13 +284,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.figure(figsize=(11,4))\n",
-    "plt.subplot(121)\n",
+    "fix, axes = plt.subplots(ncols=2, figsize=(10,4), sharey=True)\n",
+    "plt.sca(axes[0])\n",
     "plot_decision_boundary(tree_clf, X, y)\n",
     "plt.title(\"Decision Tree\", fontsize=14)\n",
-    "plt.subplot(122)\n",
+    "plt.sca(axes[1])\n",
     "plot_decision_boundary(bag_clf, X, y)\n",
     "plt.title(\"Decision Trees with Bagging\", fontsize=14)\n",
+    "plt.ylabel(\"\")\n",
     "save_fig(\"decision_tree_without_and_with_bagging_plot\")\n",
     "plt.show()"
    ]
@@ -381,7 +382,7 @@
     "    tree_clf = DecisionTreeClassifier(max_leaf_nodes=16, random_state=42 + i)\n",
     "    indices_with_replacement = np.random.randint(0, len(X_train), len(X_train))\n",
     "    tree_clf.fit(X[indices_with_replacement], y[indices_with_replacement])\n",
-    "    plot_decision_boundary(tree_clf, X, y, axes=[-1.5, 2.5, -1, 1.5], alpha=0.02, contour=False)\n",
+    "    plot_decision_boundary(tree_clf, X, y, axes=[-1.5, 2.45, -1, 1.5], alpha=0.02, contour=False)\n",
     "\n",
     "plt.show()"
    ]
@@ -521,10 +522,10 @@
    "source": [
     "m = len(X_train)\n",
     "\n",
-    "plt.figure(figsize=(11, 4))\n",
-    "for subplot, learning_rate in ((121, 1), (122, 0.5)):\n",
+    "fix, axes = plt.subplots(ncols=2, figsize=(10,4), sharey=True)\n",
+    "for subplot, learning_rate in ((0, 1), (1, 0.5)):\n",
     "    sample_weights = np.ones(m)\n",
-    "    plt.subplot(subplot)\n",
+    "    plt.sca(axes[subplot])\n",
     "    for i in range(5):\n",
     "        svm_clf = SVC(kernel=\"rbf\", C=0.05, gamma=\"scale\", random_state=42)\n",
     "        svm_clf.fit(X_train, y_train, sample_weight=sample_weights)\n",
@@ -532,12 +533,14 @@
     "        sample_weights[y_pred != y_train] *= (1 + learning_rate)\n",
     "        plot_decision_boundary(svm_clf, X, y, alpha=0.2)\n",
     "        plt.title(\"learning_rate = {}\".format(learning_rate), fontsize=16)\n",
-    "    if subplot == 121:\n",
+    "    if subplot == 0:\n",
     "        plt.text(-0.7, -0.65, \"1\", fontsize=14)\n",
     "        plt.text(-0.6, -0.10, \"2\", fontsize=14)\n",
     "        plt.text(-0.5,  0.10, \"3\", fontsize=14)\n",
     "        plt.text(-0.4,  0.55, \"4\", fontsize=14)\n",
     "        plt.text(-0.3,  0.90, \"5\", fontsize=14)\n",
+    "    else:\n",
+    "        plt.ylabel(\"\")\n",
     "\n",
     "save_fig(\"boosting_plot\")\n",
     "plt.show()"
@@ -715,15 +718,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.figure(figsize=(11,4))\n",
+    "fix, axes = plt.subplots(ncols=2, figsize=(10,4), sharey=True)\n",
     "\n",
-    "plt.subplot(121)\n",
+    "plt.sca(axes[0])\n",
     "plot_predictions([gbrt], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"Ensemble predictions\")\n",
     "plt.title(\"learning_rate={}, n_estimators={}\".format(gbrt.learning_rate, gbrt.n_estimators), fontsize=14)\n",
+    "plt.xlabel(\"$x_1$\", fontsize=16)\n",
+    "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n",
     "\n",
-    "plt.subplot(122)\n",
+    "plt.sca(axes[1])\n",
     "plot_predictions([gbrt_slow], X, y, axes=[-0.5, 0.5, -0.1, 0.8])\n",
     "plt.title(\"learning_rate={}, n_estimators={}\".format(gbrt_slow.learning_rate, gbrt_slow.n_estimators), fontsize=14)\n",
+    "plt.xlabel(\"$x_1$\", fontsize=16)\n",
     "\n",
     "save_fig(\"gbrt_learning_rate_plot\")\n",
     "plt.show()"
@@ -774,7 +780,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.figure(figsize=(11, 4))\n",
+    "plt.figure(figsize=(10, 4))\n",
     "\n",
     "plt.subplot(121)\n",
     "plt.plot(errors, \"b.-\")\n",
@@ -784,11 +790,14 @@
     "plt.text(bst_n_estimators, min_error*1.2, \"Minimum\", ha=\"center\", fontsize=14)\n",
     "plt.axis([0, 120, 0, 0.01])\n",
     "plt.xlabel(\"Number of trees\")\n",
+    "plt.ylabel(\"Error\", fontsize=16)\n",
     "plt.title(\"Validation error\", fontsize=14)\n",
     "\n",
     "plt.subplot(122)\n",
     "plot_predictions([gbrt_best], X, y, axes=[-0.5, 0.5, -0.1, 0.8])\n",
     "plt.title(\"Best model (%d trees)\" % bst_n_estimators, fontsize=14)\n",
+    "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n",
+    "plt.xlabel(\"$x_1$\", fontsize=16)\n",
     "\n",
     "save_fig(\"early_stopping_gbrt_plot\")\n",
     "plt.show()"
@@ -1200,7 +1209,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "That's a significant improvement, and it's much better than each of the individual classifiers."
+    "Nope, hard voting wins in this case."
    ]
   },
   {
@@ -1216,6 +1225,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "voting_clf.voting = \"hard\"\n",
     "voting_clf.score(X_test, y_test)"
    ]
   },
@@ -1232,7 +1242,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The voting classifier reduced the error rate from about 4.0% for our best model (the `MLPClassifier`) to just 3.1%. That's about 22.5% less errors, not bad!"
+    "The voting classifier only very slightly reduced the error rate of the best model in this case."
    ]
   },
   {
@@ -1346,8 +1356,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This stacking ensemble does not perform as well as the soft voting classifier we trained earlier, it's just as good as the best individual classifier."
+    "This stacking ensemble does not perform as well as the voting classifier we trained earlier, it's not quite as good as the best individual classifier."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -1366,7 +1383,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.7.4"
   },
   "nav_menu": {
    "height": "252px",