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diff --git a/‎dev/_sources/auto_examples/applications/plot_outlier_detection_wine.rst.txt
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diff --git a/‎dev/_sources/auto_examples/applications/plot_species_distribution_modeling.rst.txt
+2-2 b/‎dev/_sources/auto_examples/applications/plot_species_distribution_modeling.rst.txt
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diff --git a/‎dev/_sources/auto_examples/applications/plot_stock_market.rst.txt
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diff --git a/‎dev/_sources/auto_examples/applications/plot_tomography_l1_reconstruction.rst.txt
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diff --git a/‎dev/_sources/auto_examples/applications/plot_topics_extraction_with_nmf_lda.rst.txt
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@@ -80,7 +80,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\n\nplt.subplot(2, 1, 1)\nplt.semilogx(alphas, train_errors, label=\"Train\")\nplt.semilogx(alphas, test_errors, label=\"Test\")\nplt.vlines(\n    alpha_optim,\n    plt.ylim()[0],\n    np.max(test_errors),\n    color=\"k\",\n    linewidth=3,\n    label=\"Optimum on test\",\n)\nplt.legend(loc=\"lower left\")\nplt.ylim([0, 1.2])\nplt.xlabel(\"Regularization parameter\")\nplt.ylabel(\"Performance\")\n\n# Show estimated coef_ vs true coef\nplt.subplot(2, 1, 2)\nplt.plot(coef, label=\"True coef\")\nplt.plot(coef_, label=\"Estimated coef\")\nplt.legend()\nplt.subplots_adjust(0.09, 0.04, 0.94, 0.94, 0.26, 0.26)\nplt.show()"
+        "import matplotlib.pyplot as plt\n\nplt.subplot(2, 1, 1)\nplt.semilogx(alphas, train_errors, label=\"Train\")\nplt.semilogx(alphas, test_errors, label=\"Test\")\nplt.vlines(\n    alpha_optim,\n    plt.ylim()[0],\n    np.max(test_errors),\n    color=\"k\",\n    linewidth=3,\n    label=\"Optimum on test\",\n)\nplt.legend(loc=\"lower right\")\nplt.ylim([0, 1.2])\nplt.xlabel(\"Regularization parameter\")\nplt.ylabel(\"Performance\")\n\n# Show estimated coef_ vs true coef\nplt.subplot(2, 1, 2)\nplt.plot(coef, label=\"True coef\")\nplt.plot(coef_, label=\"Estimated coef\")\nplt.legend()\nplt.subplots_adjust(0.09, 0.04, 0.94, 0.94, 0.26, 0.26)\nplt.show()"
       ]
     }
   ],
 
@@ -73,7 +73,7 @@
     linewidth=3,
     label="Optimum on test",
 )
-plt.legend(loc="lower left")
+plt.legend(loc="lower right")
 plt.ylim([0, 1.2])
 plt.xlabel("Regularization parameter")
 plt.ylabel("Performance")
Original file line number	Diff line number	Diff line change
`@@ -80,7 +80,7 @@`
`80`	`80`	`},`
`81`	`81`	`"outputs": [],`
`82`	`82`	`"source": [`
`83`		- "import matplotlib.pyplot as plt\n\nplt.subplot(2, 1, 1)\nplt.semilogx(alphas, train_errors, label=\"Train\")\nplt.semilogx(alphas, test_errors, label=\"Test\")\nplt.vlines(\n alpha_optim,\n plt.ylim()[0],\n np.max(test_errors),\n color=\"k\",\n linewidth=3,\n label=\"Optimum on test\",\n)\nplt.legend(loc=\"lower left\")\nplt.ylim([0, 1.2])\nplt.xlabel(\"Regularization parameter\")\nplt.ylabel(\"Performance\")\n\n# Show estimated coef_ vs true coef\nplt.subplot(2, 1, 2)\nplt.plot(coef, label=\"True coef\")\nplt.plot(coef_, label=\"Estimated coef\")\nplt.legend()\nplt.subplots_adjust(0.09, 0.04, 0.94, 0.94, 0.26, 0.26)\nplt.show()"
	`83`	+ "import matplotlib.pyplot as plt\n\nplt.subplot(2, 1, 1)\nplt.semilogx(alphas, train_errors, label=\"Train\")\nplt.semilogx(alphas, test_errors, label=\"Test\")\nplt.vlines(\n alpha_optim,\n plt.ylim()[0],\n np.max(test_errors),\n color=\"k\",\n linewidth=3,\n label=\"Optimum on test\",\n)\nplt.legend(loc=\"lower right\")\nplt.ylim([0, 1.2])\nplt.xlabel(\"Regularization parameter\")\nplt.ylabel(\"Performance\")\n\n# Show estimated coef_ vs true coef\nplt.subplot(2, 1, 2)\nplt.plot(coef, label=\"True coef\")\nplt.plot(coef_, label=\"Estimated coef\")\nplt.legend()\nplt.subplots_adjust(0.09, 0.04, 0.94, 0.94, 0.26, 0.26)\nplt.show()"
`84`	`84`	`]`
`85`	`85`	`}`
`86`	`86`	`],`
Original file line number	Diff line number	Diff line change
`@@ -73,7 +73,7 @@`
`73`	`73`	`linewidth=3,`
`74`	`74`	`label="Optimum on test",`
`75`	`75`	`)`
`76`		`-plt.legend(loc="lower left")`
	`76`	`+plt.legend(loc="lower right")`
`77`	`77`	`plt.ylim([0, 1.2])`
`78`	`78`	`plt.xlabel("Regularization parameter")`
`79`	`79`	`plt.ylabel("Performance")`