Pushing the docs to dev/ for branch: main, commit 9f7a0e6cbfc20169394379f70fec3f260fa3770a

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -704,7 +704,7 @@
       },
       "outputs": [],
       "source": [
-        "fig, axes = plt.subplots(ncols=3, figsize=(12, 4), sharey=True)\nfig.suptitle(\"Non-linear regression models\")\npredictions = [\n    one_hot_poly_predictions,\n    cyclic_spline_poly_predictions,\n    gbrt_predictions,\n]\nlabels = [\n    \"One hot + polynomial kernel\",\n    \"Splines + polynomial kernel\",\n    \"Gradient Boosted Trees\",\n]\nfor ax, pred, label in zip(axes, predictions, labels):\n    ax.scatter(y.iloc[test_0].values, pred, alpha=0.3, label=label)\n    ax.plot([0, 1], [0, 1], \"--\", label=\"Perfect model\")\n    ax.set(\n        xlim=(0, 1),\n        ylim=(0, 1),\n        xlabel=\"True demand\",\n        ylabel=\"Predicted demand\",\n    )\n    ax.legend()"
+        "fig, axes = plt.subplots(ncols=3, figsize=(12, 4), sharey=True)\nfig.suptitle(\"Non-linear regression models\")\npredictions = [\n    one_hot_poly_predictions,\n    cyclic_spline_poly_predictions,\n    gbrt_predictions,\n]\nlabels = [\n    \"One hot + polynomial kernel\",\n    \"Splines + polynomial kernel\",\n    \"Gradient Boosted Trees\",\n]\nfor ax, pred, label in zip(axes, predictions, labels):\n    ax.scatter(y.iloc[test_0].values, pred, alpha=0.3, label=label)\n    ax.plot([0, 1], [0, 1], \"--\", label=\"Perfect model\")\n    ax.set(\n        xlim=(0, 1),\n        ylim=(0, 1),\n        xlabel=\"True demand\",\n        ylabel=\"Predicted demand\",\n    )\n    ax.legend()\n\nplt.show()"
       ]
     },
     {

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -791,7 +791,7 @@ def periodic_spline_transformer(period, n_splines=None, degree=3):
     )
     ax.legend()
 
-
+plt.show()
 # %%
 # This visualization confirms the conclusions we draw on the previous plot.
 #