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nesi · Nov 11, 2024 · 4fd0340 · 4fd0340
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diff --git a/notebooks/03.2-Regression-Forests.ipynb b/notebooks/03.2-Regression-Forests.ipynb
@@ -409,6 +409,13 @@
     "\n",
     "Repeat this classification task with ``sklearn.ensemble.RandomForestClassifier``.  How does the ``max_depth``, ``max_features``, and ``n_estimators`` affect the results?"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

diff --git a/notebooks/04.1-Dimensionality-PCA.ipynb b/notebooks/04.1-Dimensionality-PCA.ipynb
@@ -183,7 +183,7 @@
    "outputs": [],
    "source": [
     "plt.scatter(Xproj[:, 0], Xproj[:, 1], c=y, edgecolor='none', alpha=0.5,\n",
-    "            cmap=plt.cm.get_cmap('nipy_spectral', 10))\n",
+    "            cmap=plt.colormaps.get_cmap('nipy_spectral'))\n",
     "plt.colorbar();"
    ]
   },
@@ -380,6 +380,13 @@
     "   \n",
     "Each of these has its own strengths & weaknesses, and areas of application. You can read about them on the [scikit-learn website](http://sklearn.org)."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

diff --git a/notebooks/04.2-Clustering-KMeans.ipynb b/notebooks/04.2-Clustering-KMeans.ipynb
@@ -81,7 +81,7 @@
    "outputs": [],
    "source": [
     "from sklearn.cluster import KMeans\n",
-    "est = KMeans(4)  # 4 clusters\n",
+    "est = KMeans(4, n_init='auto')  # 4 clusters\n",
     "est.fit(X)\n",
     "y_kmeans = est.predict(X)\n",
     "plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=50, cmap='rainbow');"
@@ -164,7 +164,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "est = KMeans(n_clusters=10)\n",
+    "est = KMeans(n_clusters=10, n_init='auto')\n",
     "clusters = est.fit_predict(digits.data)\n",
     "est.cluster_centers_.shape"
    ]
@@ -208,7 +208,7 @@
     "labels = np.zeros_like(clusters)\n",
     "for i in range(10):\n",
     "    mask = (clusters == i)\n",
-    "    labels[mask] = mode(digits.target[mask])[0]"
+    "    labels[mask] = mode(digits.target[mask], keepdims=False)[0]"
    ]
   },
   {
@@ -228,7 +228,7 @@
     "\n",
     "X = PCA(2).fit_transform(digits.data)\n",
     "\n",
-    "kwargs = dict(cmap = plt.cm.get_cmap('rainbow', 10),\n",
+    "kwargs = dict(cmap=plt.colormaps.get_cmap('rainbow'),\n",
     "              edgecolor='none', alpha=0.6)\n",
     "fig, ax = plt.subplots(1, 2, figsize=(8, 4))\n",
     "ax[0].scatter(X[:, 0], X[:, 1], c=labels, **kwargs)\n",
@@ -368,7 +368,7 @@
     "\n",
     "X = (china / 255.0).reshape(-1, 3)\n",
     "    \n",
-    "model = MiniBatchKMeans(n_colors)\n",
+    "model = MiniBatchKMeans(n_colors, n_init='auto')\n",
     "labels = model.fit_predict(X)\n",
     "colors = model.cluster_centers_\n",
     "new_image = colors[labels].reshape(china.shape)\n",

diff --git a/notebooks/05-Validation.ipynb b/notebooks/05-Validation.ipynb
@@ -825,6 +825,13 @@
     "  \n",
     "These tools are powerful means of evaluating your model on your data."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

diff --git a/notebooks/06.1-Practice-Penguins.ipynb b/notebooks/06.1-Practice-Penguins.ipynb
@@ -297,6 +297,14 @@
    "source": [
     "# create a confusion matrix for cross-validated predictions\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3488c51c-4e0d-45ec-92aa-c791d14915d4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {