Add dual=True to LinearSVC and parser='auto' to fetch_openml

07_ensemble_learning_and_random_forests.ipynb

@@ -636,7 +636,8 @@
     "\n",
     "from sklearn.datasets import fetch_openml\n",
     "\n",
-    "X_mnist, y_mnist = fetch_openml('mnist_784', return_X_y=True, as_frame=False)\n",
+    "X_mnist, y_mnist = fetch_openml('mnist_784', return_X_y=True, as_frame=False,\n",
+    "                                parser='auto')\n",
     "\n",
     "rnd_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
     "rnd_clf.fit(X_mnist, y_mnist)\n",
@@ -1305,6 +1306,13 @@
     "from sklearn.neural_network import MLPClassifier"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note: The `LinearSVC` has a `dual` hyperparameter whose default value will change from `True` to `\"auto\"` in Scikit-Learn 1.5. To ensure this notebook continues to produce the same outputs, I'm setting it explicitly to `True`. Please see the [documentation](https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html) for more details."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 42,
@@ -1313,7 +1321,7 @@
    "source": [
     "random_forest_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
     "extra_trees_clf = ExtraTreesClassifier(n_estimators=100, random_state=42)\n",
-    "svm_clf = LinearSVC(max_iter=100, tol=20, random_state=42)\n",
+    "svm_clf = LinearSVC(max_iter=100, tol=20, dual=True, random_state=42)\n",
     "mlp_clf = MLPClassifier(random_state=42)"
    ]
   },
@@ -2045,7 +2053,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -2059,7 +2067,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.13"
   },
   "nav_menu": {
    "height": "252px",