Use as_frame=False when calling fetch_openml()

05_support_vector_machines.ipynb

@@ -1381,6 +1381,13 @@
     "First, let's load the dataset and split it into a training set and a test set. We could use `train_test_split()` but people usually just take the first 60,000 instances for the training set, and the last 10,000 instances for the test set (this makes it possible to compare your model's performance with others): "
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Warning:** since Scikit-Learn 0.24, `fetch_openml()` returns a Pandas `DataFrame` by default. To avoid this, we use `as_frame=False`."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 47,
@@ -1388,7 +1395,7 @@
    "outputs": [],
    "source": [
     "from sklearn.datasets import fetch_openml\n",
-    "mnist = fetch_openml('mnist_784', version=1, cache=True)\n",
+    "mnist = fetch_openml('mnist_784', version=1, cache=True, as_frame=False)\n",
     "\n",
     "X = mnist[\"data\"]\n",
     "y = mnist[\"target\"].astype(np.uint8)\n",
@@ -1837,7 +1844,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.8"
+   "version": "3.7.9"
   },
   "nav_menu": {},
   "toc": {