diff --git a/09_unsupervised_learning.ipynb b/09_unsupervised_learning.ipynb
index fc1ea89..ebbf3ac 100644
--- a/09_unsupervised_learning.ipynb
+++ b/09_unsupervised_learning.ipynb
@@ -230,7 +230,7 @@
     "mapping = {}\n",
     "for class_id in np.unique(y):\n",
     "    mode, _ = stats.mode(y_pred[y==class_id])\n",
-    "    mapping[mode[0]] = class_id\n",
+    "    mapping[mode] = class_id\n",
     "\n",
     "y_pred = np.array([mapping[cluster_id] for cluster_id in y_pred])\n",
     "\n",
@@ -309,10 +309,17 @@
     "                  random_state=7)\n",
     "\n",
     "k = 5\n",
-    "kmeans = KMeans(n_clusters=k, random_state=42)\n",
+    "kmeans = KMeans(n_clusters=k, n_init=10, random_state=42)\n",
     "y_pred = kmeans.fit_predict(X)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note: Throughout this notebook, when `n_init` was not set when creating a `KMeans` estimator, I explicitly set it to `n_init=10` to avoid a warning about the fact that the default value for this hyperparameter will change from 10 to `\"auto\"` in Scikit-Learn 1.4."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1169,10 +1176,17 @@
    "source": [
     "from sklearn.cluster import MiniBatchKMeans\n",
     "\n",
-    "minibatch_kmeans = MiniBatchKMeans(n_clusters=5, random_state=42)\n",
+    "minibatch_kmeans = MiniBatchKMeans(n_clusters=5, n_init=3, random_state=42)\n",
     "minibatch_kmeans.fit(X)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note: Throughout this notebook, when `n_init` was not set when creating a `MiniBatchKMeans` estimator, I explicitly set it to `n_init=3` to avoid a warning about the fact that the default value for this hyperparameter will change from 3 to `\"auto\"` in Scikit-Learn 1.4."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 31,
@@ -1215,7 +1229,7 @@
    "source": [
     "from sklearn.datasets import fetch_openml\n",
     "\n",
-    "mnist = fetch_openml('mnist_784', as_frame=False)"
+    "mnist = fetch_openml('mnist_784', as_frame=False, parser=\"auto\")"
    ]
   },
   {
@@ -1275,7 +1289,7 @@
     "from sklearn.cluster import MiniBatchKMeans\n",
     "\n",
     "minibatch_kmeans = MiniBatchKMeans(n_clusters=10, batch_size=10,\n",
-    "                                   random_state=42)\n",
+    "                                   n_init=3, random_state=42)\n",
     "minibatch_kmeans.fit(X_memmap)"
    ]
   },
@@ -1320,8 +1334,8 @@
     "times = np.empty((max_k, 2))\n",
     "inertias = np.empty((max_k, 2))\n",
     "for k in range(1, max_k + 1):\n",
-    "    kmeans_ = KMeans(n_clusters=k, algorithm=\"full\", random_state=42)\n",
-    "    minibatch_kmeans = MiniBatchKMeans(n_clusters=k, random_state=42)\n",
+    "    kmeans_ = KMeans(n_clusters=k, algorithm=\"lloyd\", n_init=10, random_state=42)\n",
+    "    minibatch_kmeans = MiniBatchKMeans(n_clusters=k, n_init=10, random_state=42)\n",
     "    print(f\"\\r{k}/{max_k}\", end=\"\")  # \\r returns to the start of line\n",
     "    times[k - 1, 0] = timeit(\"kmeans_.fit(X)\", number=10, globals=globals())\n",
     "    times[k - 1, 1] = timeit(\"minibatch_kmeans.fit(X)\", number=10,\n",
@@ -1387,8 +1401,8 @@
    "source": [
     "# extra code – this cell generates and saves Figure 9–7\n",
     "\n",
-    "kmeans_k3 = KMeans(n_clusters=3, random_state=42)\n",
-    "kmeans_k8 = KMeans(n_clusters=8, random_state=42)\n",
+    "kmeans_k3 = KMeans(n_clusters=3, n_init=10, random_state=42)\n",
+    "kmeans_k8 = KMeans(n_clusters=8, n_init=10, random_state=42)\n",
     "\n",
     "plot_clusterer_comparison(kmeans_k3, kmeans_k8, X, \"$k=3$\", \"$k=8$\")\n",
     "save_fig(\"bad_n_clusters_plot\")\n",
@@ -1470,7 +1484,7 @@
    "source": [
     "# extra code – this cell generates and saves Figure 9–8\n",
     "\n",
-    "kmeans_per_k = [KMeans(n_clusters=k, random_state=42).fit(X)\n",
+    "kmeans_per_k = [KMeans(n_clusters=k, n_init=10, random_state=42).fit(X)\n",
     "                for k in range(1, 10)]\n",
     "inertias = [model.inertia_ for model in kmeans_per_k]\n",
     "\n",
@@ -1724,7 +1738,7 @@
     "kmeans_good = KMeans(n_clusters=3,\n",
     "                     init=np.array([[-1.5, 2.5], [0.5, 0], [4, 0]]),\n",
     "                     n_init=1, random_state=42)\n",
-    "kmeans_bad = KMeans(n_clusters=3, random_state=42)\n",
+    "kmeans_bad = KMeans(n_clusters=3, n_init=10, random_state=42)\n",
     "kmeans_good.fit(X)\n",
     "kmeans_bad.fit(X)\n",
     "\n",
@@ -1805,7 +1819,7 @@
    "outputs": [],
    "source": [
     "X = image.reshape(-1, 3)\n",
-    "kmeans = KMeans(n_clusters=8, random_state=42).fit(X)\n",
+    "kmeans = KMeans(n_clusters=8, n_init=10, random_state=42).fit(X)\n",
     "segmented_img = kmeans.cluster_centers_[kmeans.labels_]\n",
     "segmented_img = segmented_img.reshape(image.shape)"
    ]
@@ -1834,7 +1848,7 @@
     "segmented_imgs = []\n",
     "n_colors = (10, 8, 6, 4, 2)\n",
     "for n_clusters in n_colors:\n",
-    "    kmeans = KMeans(n_clusters=n_clusters, random_state=42).fit(X)\n",
+    "    kmeans = KMeans(n_clusters=n_clusters, n_init=10, random_state=42).fit(X)\n",
     "    segmented_img = kmeans.cluster_centers_[kmeans.labels_]\n",
     "    segmented_imgs.append(segmented_img.reshape(image.shape))\n",
     "\n",
@@ -1978,7 +1992,7 @@
    "outputs": [],
    "source": [
     "k = 50\n",
-    "kmeans = KMeans(n_clusters=k, random_state=42)\n",
+    "kmeans = KMeans(n_clusters=k, n_init=10, random_state=42)\n",
     "X_digits_dist = kmeans.fit_transform(X_train)\n",
     "representative_digit_idx = X_digits_dist.argmin(axis=0)\n",
     "X_representative_digits = X_train[representative_digit_idx]"
@@ -2623,8 +2637,7 @@
    "source": [
     "def plot_spectral_clustering(sc, X, size, alpha, show_xlabels=True,\n",
     "                             show_ylabels=True):\n",
-    "    plt.scatter(X[:, 0], X[:, 1], marker='o', s=size, c='gray', cmap=\"Paired\",\n",
-    "                alpha=alpha)\n",
+    "    plt.scatter(X[:, 0], X[:, 1], marker='o', s=size, c='gray', alpha=alpha)\n",
     "    plt.scatter(X[:, 0], X[:, 1], marker='o', s=30, c='w')\n",
     "    plt.scatter(X[:, 0], X[:, 1], marker='.', s=10, c=sc.labels_, cmap=\"Paired\")\n",
     "    \n",
@@ -4005,7 +4018,7 @@
     "kmeans_per_k = []\n",
     "for k in k_range:\n",
     "    print(f\"k={k}\")\n",
-    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
+    "    kmeans = KMeans(n_clusters=k, n_init=10, random_state=42)\n",
     "    kmeans.fit(X_train_pca)\n",
     "    kmeans_per_k.append(kmeans)"
    ]
@@ -6581,7 +6594,7 @@
     "\n",
     "for n_clusters in k_range:\n",
     "    pipeline = make_pipeline(\n",
-    "        KMeans(n_clusters=n_clusters, random_state=42),\n",
+    "        KMeans(n_clusters=n_clusters, n_init=10, random_state=42),\n",
     "        RandomForestClassifier(n_estimators=150, random_state=42)\n",
     "    )\n",
     "    pipeline.fit(X_train_pca, y_train)\n",
@@ -6971,7 +6984,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -6985,7 +6998,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,