scikit-learn
diff --git a/‎dev/_downloads/auto_examples_jupyter.zip
-53 Bytes b/‎dev/_downloads/auto_examples_jupyter.zip
-53 Bytes
diff --git a/‎dev/_downloads/auto_examples_python.zip
-51 Bytes b/‎dev/_downloads/auto_examples_python.zip
-51 Bytes
diff --git a/‎dev/_downloads/plot_gradient_boosting_early_stopping.ipynb
Lines changed: 1 addition & 1 deletion b/‎dev/_downloads/plot_gradient_boosting_early_stopping.ipynb
Lines changed: 1 addition & 1 deletion
diff --git a/‎dev/_downloads/plot_gradient_boosting_early_stopping.py
Lines changed: 0 additions & 2 deletions b/‎dev/_downloads/plot_gradient_boosting_early_stopping.py
Lines changed: 0 additions & 2 deletions
diff --git a/‎dev/_downloads/scikit-learn-docs.pdf
-3.74 KB b/‎dev/_downloads/scikit-learn-docs.pdf
-3.74 KB
diff --git a/‎dev/_images/sphx_glr_plot_agglomerative_clustering_003.png
86 Bytes b/‎dev/_images/sphx_glr_plot_agglomerative_clustering_003.png
86 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_agglomerative_clustering_0031.png
86 Bytes b/‎dev/_images/sphx_glr_plot_agglomerative_clustering_0031.png
86 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_agglomerative_clustering_004.png
103 Bytes b/‎dev/_images/sphx_glr_plot_agglomerative_clustering_004.png
103 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_agglomerative_clustering_0041.png
103 Bytes b/‎dev/_images/sphx_glr_plot_agglomerative_clustering_0041.png
103 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_cluster_comparison_001.png
297 Bytes b/‎dev/_images/sphx_glr_plot_cluster_comparison_001.png
297 Bytes
@@ -44,7 +44,7 @@
       },
       "outputs": [],
       "source": [
-        "plt.figure(figsize=(9, 5))\n\nbar1 = plt.bar(index, score_gb, bar_width, label='Without early stopping',\n               color='crimson')\nbar2 = plt.bar(index + bar_width, score_gbes, bar_width,\n               label='With early stopping', color='coral')\n\nmax_y = np.amax(np.maximum(score_gb, score_gbes))\n\nplt.xticks(index + bar_width, names)\nplt.yticks(np.arange(0, 1.3, 0.1))\n\n\ndef autolabel(rects, n_estimators):\n    \"\"\"\n    Attach a text label above each bar displaying n_estimators of each model\n    \"\"\"\n    for i, rect in enumerate(rects):\n        plt.text(rect.get_x() + rect.get_width() / 2.,\n                 1.05 * rect.get_height(), 'n_est=%d' % n_estimators[i],\n                 ha='center', va='bottom')\n\n\nautolabel(bar1, n_gb)\nautolabel(bar2, n_gbes)\n\nplt.ylim([0, 1.3])\nplt.legend(loc='best')\nplt.grid(True)\n\nplt.xlabel('Datasets')\nplt.ylabel('Test score')\n\nplt.show()"
+        "plt.figure(figsize=(9, 5))\n\nbar1 = plt.bar(index, score_gb, bar_width, label='Without early stopping',\n               color='crimson')\nbar2 = plt.bar(index + bar_width, score_gbes, bar_width,\n               label='With early stopping', color='coral')\n\nplt.xticks(index + bar_width, names)\nplt.yticks(np.arange(0, 1.3, 0.1))\n\n\ndef autolabel(rects, n_estimators):\n    \"\"\"\n    Attach a text label above each bar displaying n_estimators of each model\n    \"\"\"\n    for i, rect in enumerate(rects):\n        plt.text(rect.get_x() + rect.get_width() / 2.,\n                 1.05 * rect.get_height(), 'n_est=%d' % n_estimators[i],\n                 ha='center', va='bottom')\n\n\nautolabel(bar1, n_gb)\nautolabel(bar2, n_gbes)\n\nplt.ylim([0, 1.3])\nplt.legend(loc='best')\nplt.grid(True)\n\nplt.xlabel('Datasets')\nplt.ylabel('Test score')\n\nplt.show()"
       ]
     },
     {
 
@@ -102,8 +102,6 @@
 bar2 = plt.bar(index + bar_width, score_gbes, bar_width,
                label='With early stopping', color='coral')
 
-max_y = np.amax(np.maximum(score_gb, score_gbes))
-
 plt.xticks(index + bar_width, names)
 plt.yticks(np.arange(0, 1.3, 0.1))
Original file line number	Diff line number	Diff line change
`@@ -44,7 +44,7 @@`
`44`	`44`	`},`
`45`	`45`	`"outputs": [],`
`46`	`46`	`"source": [`
`47`		- "plt.figure(figsize=(9, 5))\n\nbar1 = plt.bar(index, score_gb, bar_width, label='Without early stopping',\n color='crimson')\nbar2 = plt.bar(index + bar_width, score_gbes, bar_width,\n label='With early stopping', color='coral')\n\nmax_y = np.amax(np.maximum(score_gb, score_gbes))\n\nplt.xticks(index + bar_width, names)\nplt.yticks(np.arange(0, 1.3, 0.1))\n\n\ndef autolabel(rects, n_estimators):\n \"\"\"\n Attach a text label above each bar displaying n_estimators of each model\n \"\"\"\n for i, rect in enumerate(rects):\n plt.text(rect.get_x() + rect.get_width() / 2.,\n 1.05 * rect.get_height(), 'n_est=%d' % n_estimators[i],\n ha='center', va='bottom')\n\n\nautolabel(bar1, n_gb)\nautolabel(bar2, n_gbes)\n\nplt.ylim([0, 1.3])\nplt.legend(loc='best')\nplt.grid(True)\n\nplt.xlabel('Datasets')\nplt.ylabel('Test score')\n\nplt.show()"
	`47`	+ "plt.figure(figsize=(9, 5))\n\nbar1 = plt.bar(index, score_gb, bar_width, label='Without early stopping',\n color='crimson')\nbar2 = plt.bar(index + bar_width, score_gbes, bar_width,\n label='With early stopping', color='coral')\n\nplt.xticks(index + bar_width, names)\nplt.yticks(np.arange(0, 1.3, 0.1))\n\n\ndef autolabel(rects, n_estimators):\n \"\"\"\n Attach a text label above each bar displaying n_estimators of each model\n \"\"\"\n for i, rect in enumerate(rects):\n plt.text(rect.get_x() + rect.get_width() / 2.,\n 1.05 * rect.get_height(), 'n_est=%d' % n_estimators[i],\n ha='center', va='bottom')\n\n\nautolabel(bar1, n_gb)\nautolabel(bar2, n_gbes)\n\nplt.ylim([0, 1.3])\nplt.legend(loc='best')\nplt.grid(True)\n\nplt.xlabel('Datasets')\nplt.ylabel('Test score')\n\nplt.show()"
`48`	`48`	`]`
`49`	`49`	`},`
`50`	`50`	`{`