Pushing the docs to dev/ for branch: main, commit 58263588fd91e0fb4d3e7360143920d4ba3d9763

Author: sklearn-ci

dev/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: aaa03407e565205bf8dcc7059fb30973
+config: 6fc38dc842f8ee47e0363416a338bea9
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -87,7 +87,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\n\nfig, ax = plt.subplots()\n\nax.hist(perm_scores_iris, bins=20, density=True)\nax.axvline(score_iris, ls=\"--\", color=\"r\")\nscore_label = f\"Score on original\\ndata: {score_iris:.2f}\\n(p-value: {pvalue_iris:.3f})\"\nax.text(0.7, 10, score_label, fontsize=12)\nax.set_xlabel(\"Accuracy score\")\n_ = ax.set_ylabel(\"Probability\")"
+        "import matplotlib.pyplot as plt\n\nfig, ax = plt.subplots()\n\nax.hist(perm_scores_iris, bins=20, density=True)\nax.axvline(score_iris, ls=\"--\", color=\"r\")\nscore_label = f\"Score on original\\ndata: {score_iris:.2f}\\n(p-value: {pvalue_iris:.3f})\"\nax.text(0.7, 10, score_label, fontsize=12)\nax.set_xlabel(\"Accuracy score\")\n_ = ax.set_ylabel(\"Probability density\")"
       ]
     },
     {
@@ -105,7 +105,7 @@
       },
       "outputs": [],
       "source": [
-        "fig, ax = plt.subplots()\n\nax.hist(perm_scores_rand, bins=20, density=True)\nax.set_xlim(0.13)\nax.axvline(score_rand, ls=\"--\", color=\"r\")\nscore_label = f\"Score on original\\ndata: {score_rand:.2f}\\n(p-value: {pvalue_rand:.3f})\"\nax.text(0.14, 7.5, score_label, fontsize=12)\nax.set_xlabel(\"Accuracy score\")\nax.set_ylabel(\"Probability\")\nplt.show()"
+        "fig, ax = plt.subplots()\n\nax.hist(perm_scores_rand, bins=20, density=True)\nax.set_xlim(0.13)\nax.axvline(score_rand, ls=\"--\", color=\"r\")\nscore_label = f\"Score on original\\ndata: {score_rand:.2f}\\n(p-value: {pvalue_rand:.3f})\"\nax.text(0.14, 7.5, score_label, fontsize=12)\nax.set_xlabel(\"Accuracy score\")\nax.set_ylabel(\"Probability density\")\nplt.show()"
       ]
     },
     {

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -95,7 +95,7 @@
 score_label = f"Score on original\ndata: {score_iris:.2f}\n(p-value: {pvalue_iris:.3f})"
 ax.text(0.7, 10, score_label, fontsize=12)
 ax.set_xlabel("Accuracy score")
-_ = ax.set_ylabel("Probability")
+_ = ax.set_ylabel("Probability density")
 
 # %%
 # Random data
@@ -116,7 +116,7 @@
 score_label = f"Score on original\ndata: {score_rand:.2f}\n(p-value: {pvalue_rand:.3f})"
 ax.text(0.14, 7.5, score_label, fontsize=12)
 ax.set_xlabel("Accuracy score")
-ax.set_ylabel("Probability")
+ax.set_ylabel("Probability density")
 plt.show()
 
 # %%