Pushing the docs to dev/ for branch: main, commit cc97b80fd0836a7123cbcf3893152eccd643f448

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -58,7 +58,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nmdi_importances = pd.Series(clf.feature_importances_, index=X_train.columns)\ntree_importance_sorted_idx = np.argsort(clf.feature_importances_)\ntree_indices = np.arange(0, len(clf.feature_importances_)) + 0.5\n\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 8))\nmdi_importances.sort_values().plot.barh(ax=ax1)\nax1.set_xlabel(\"Gini importance\")\nplot_permutation_importance(clf, X_train, y_train, ax2)\nax2.set_xlabel(\"Decrease in accuracy score\")\nfig.suptitle(\n    \"Impurity-based vs. permutation importances on multicollinear features (train set)\"\n)\n_ = fig.tight_layout()"
+        "import matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nmdi_importances = pd.Series(clf.feature_importances_, index=X_train.columns)\ntree_importance_sorted_idx = np.argsort(clf.feature_importances_)\n\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 8))\nmdi_importances.sort_values().plot.barh(ax=ax1)\nax1.set_xlabel(\"Gini importance\")\nplot_permutation_importance(clf, X_train, y_train, ax2)\nax2.set_xlabel(\"Decrease in accuracy score\")\nfig.suptitle(\n    \"Impurity-based vs. permutation importances on multicollinear features (train set)\"\n)\n_ = fig.tight_layout()"
       ]
     },
     {

dev/_downloads/4941b506cc56c9cec00d40992e2a4207/plot_permutation_importance_multicollinear.ipynb

@@ -66,7 +66,6 @@ def plot_permutation_importance(clf, X, y, ax):
 
 mdi_importances = pd.Series(clf.feature_importances_, index=X_train.columns)
 tree_importance_sorted_idx = np.argsort(clf.feature_importances_)
-tree_indices = np.arange(0, len(clf.feature_importances_)) + 0.5
 
 fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 8))
 mdi_importances.sort_values().plot.barh(ax=ax1)