Pushing the docs to dev/ for branch: main, commit 378cddd08281efd2a70e960dca7a95ab3b3e9771

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -304,6 +304,7 @@ def get_impute_iterative(X_missing, y_missing):
 
 plt.show()
 
+# %%
 # You can also try different techniques. For instance, the median is a more
 # robust estimator for data with high magnitude variables which could dominate
 # results (otherwise known as a 'long tail').

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -188,7 +188,14 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\n\n\nn_bars = len(mses_diabetes)\nxval = np.arange(n_bars)\n\ncolors = [\"r\", \"g\", \"b\", \"orange\", \"black\"]\n\n# plot diabetes results\nplt.figure(figsize=(12, 6))\nax1 = plt.subplot(121)\nfor j in xval:\n    ax1.barh(\n        j,\n        mses_diabetes[j],\n        xerr=stds_diabetes[j],\n        color=colors[j],\n        alpha=0.6,\n        align=\"center\",\n    )\n\nax1.set_title(\"Imputation Techniques with Diabetes Data\")\nax1.set_xlim(left=np.min(mses_diabetes) * 0.9, right=np.max(mses_diabetes) * 1.1)\nax1.set_yticks(xval)\nax1.set_xlabel(\"MSE\")\nax1.invert_yaxis()\nax1.set_yticklabels(x_labels)\n\n# plot california dataset results\nax2 = plt.subplot(122)\nfor j in xval:\n    ax2.barh(\n        j,\n        mses_california[j],\n        xerr=stds_california[j],\n        color=colors[j],\n        alpha=0.6,\n        align=\"center\",\n    )\n\nax2.set_title(\"Imputation Techniques with California Data\")\nax2.set_yticks(xval)\nax2.set_xlabel(\"MSE\")\nax2.invert_yaxis()\nax2.set_yticklabels([\"\"] * n_bars)\n\nplt.show()\n\n# You can also try different techniques. For instance, the median is a more\n# robust estimator for data with high magnitude variables which could dominate\n# results (otherwise known as a 'long tail')."
+        "import matplotlib.pyplot as plt\n\n\nn_bars = len(mses_diabetes)\nxval = np.arange(n_bars)\n\ncolors = [\"r\", \"g\", \"b\", \"orange\", \"black\"]\n\n# plot diabetes results\nplt.figure(figsize=(12, 6))\nax1 = plt.subplot(121)\nfor j in xval:\n    ax1.barh(\n        j,\n        mses_diabetes[j],\n        xerr=stds_diabetes[j],\n        color=colors[j],\n        alpha=0.6,\n        align=\"center\",\n    )\n\nax1.set_title(\"Imputation Techniques with Diabetes Data\")\nax1.set_xlim(left=np.min(mses_diabetes) * 0.9, right=np.max(mses_diabetes) * 1.1)\nax1.set_yticks(xval)\nax1.set_xlabel(\"MSE\")\nax1.invert_yaxis()\nax1.set_yticklabels(x_labels)\n\n# plot california dataset results\nax2 = plt.subplot(122)\nfor j in xval:\n    ax2.barh(\n        j,\n        mses_california[j],\n        xerr=stds_california[j],\n        color=colors[j],\n        alpha=0.6,\n        align=\"center\",\n    )\n\nax2.set_title(\"Imputation Techniques with California Data\")\nax2.set_yticks(xval)\nax2.set_xlabel(\"MSE\")\nax2.invert_yaxis()\nax2.set_yticklabels([\"\"] * n_bars)\n\nplt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can also try different techniques. For instance, the median is a more\nrobust estimator for data with high magnitude variables which could dominate\nresults (otherwise known as a 'long tail').\n\n"
       ]
     }
   ],