Pushing the docs to dev/ for branch: main, commit 6a7c8fd565aba9372aedde8e8b81f2b927ca7a12

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: df6c478a5a060e86affdeb3d97c925a9
+config: 9ffd15765005bbd1e9736a397cbe3bb7
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/006fc185672e58b056a5c134db26935c/plot_coin_segmentation.ipynb

@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "# Computing a few extra eigenvectors may speed up the eigen_solver.\n# The spectral clustering quality may also benetif from requesting\n# extra regions for segmentation.\nn_regions_plus = 3\n\n# Apply spectral clustering using the default eigen_solver='arpack'.\n# Any implemented solver can be used: eigen_solver='arpack', 'lobpcg', or 'amg'.\n# Choosing eigen_solver='amg' requires an extra package called 'pyamg'.\n# The quality of segmentation and the speed of calculations is mostly determined\n# by the choice of the solver and the value of the tolerance 'eigen_tol'.\n# TODO: varying eigen_tol seems to have no effect for 'lobpcg' and 'amg' #21243.\nfor assign_labels in (\"kmeans\", \"discretize\", \"cluster_qr\"):\n    t0 = time.time()\n    labels = spectral_clustering(\n        graph,\n        n_clusters=(n_regions + n_regions_plus),\n        eigen_tol=1e-7,\n        assign_labels=assign_labels,\n        random_state=42,\n    )\n\n    t1 = time.time()\n    labels = labels.reshape(rescaled_coins.shape)\n    plt.figure(figsize=(5, 5))\n    plt.imshow(rescaled_coins, cmap=plt.cm.gray)\n\n    plt.xticks(())\n    plt.yticks(())\n    title = \"Spectral clustering: %s, %.2fs\" % (assign_labels, (t1 - t0))\n    print(title)\n    plt.title(title)\n    for l in range(n_regions):\n        colors = [plt.cm.nipy_spectral((l + 4) / float(n_regions + 4))]\n        plt.contour(labels == l, colors=colors)\n        # To view individual segments as appear comment in plt.pause(0.5)\nplt.show()\n\n# TODO: After #21194 is merged and #21243 is fixed, check which eigen_solver\n# is the best and set eigen_solver='arpack', 'lobpcg', or 'amg' and eigen_tol\n# explicitly in this example."
+        "# Computing a few extra eigenvectors may speed up the eigen_solver.\n# The spectral clustering quality may also benefit from requesting\n# extra regions for segmentation.\nn_regions_plus = 3\n\n# Apply spectral clustering using the default eigen_solver='arpack'.\n# Any implemented solver can be used: eigen_solver='arpack', 'lobpcg', or 'amg'.\n# Choosing eigen_solver='amg' requires an extra package called 'pyamg'.\n# The quality of segmentation and the speed of calculations is mostly determined\n# by the choice of the solver and the value of the tolerance 'eigen_tol'.\n# TODO: varying eigen_tol seems to have no effect for 'lobpcg' and 'amg' #21243.\nfor assign_labels in (\"kmeans\", \"discretize\", \"cluster_qr\"):\n    t0 = time.time()\n    labels = spectral_clustering(\n        graph,\n        n_clusters=(n_regions + n_regions_plus),\n        eigen_tol=1e-7,\n        assign_labels=assign_labels,\n        random_state=42,\n    )\n\n    t1 = time.time()\n    labels = labels.reshape(rescaled_coins.shape)\n    plt.figure(figsize=(5, 5))\n    plt.imshow(rescaled_coins, cmap=plt.cm.gray)\n\n    plt.xticks(())\n    plt.yticks(())\n    title = \"Spectral clustering: %s, %.2fs\" % (assign_labels, (t1 - t0))\n    print(title)\n    plt.title(title)\n    for l in range(n_regions):\n        colors = [plt.cm.nipy_spectral((l + 4) / float(n_regions + 4))]\n        plt.contour(labels == l, colors=colors)\n        # To view individual segments as appear comment in plt.pause(0.5)\nplt.show()\n\n# TODO: After #21194 is merged and #21243 is fixed, check which eigen_solver\n# is the best and set eigen_solver='arpack', 'lobpcg', or 'amg' and eigen_tol\n# explicitly in this example."
       ]
     }
   ],

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -65,7 +65,7 @@
 # Compute and visualize the resulting regions
 
 # Computing a few extra eigenvectors may speed up the eigen_solver.
-# The spectral clustering quality may also benetif from requesting
+# The spectral clustering quality may also benefit from requesting
 # extra regions for segmentation.
 n_regions_plus = 3
 

dev/_downloads/2e86a4838807f09bbbb529d9643d45ab/plot_coin_segmentation.py

@@ -275,7 +275,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "All is well. We are now ready to do some predictive modeling!\n\n## Gradient Boosting\n\nGradient Boosting Regression with decision trees is often flexible enough to\nefficiently handle heterogenous tabular data with a mix of categorical and\nnumerical features as long as the number of samples is large enough.\n\nHere, we use the modern\n:class:`~sklearn.ensemble.HistGradientBoostingRegressor` with native support\nfor categorical features. Therefore, we only need to set\n`categorical_features=\"from_dtype\"` such that features with categorical dtype\nare considered categorical features. For reference, we extract the categorical\nfeatures from the dataframe based on the dtype. The internal trees use a dedicated\ntree splitting rule for these features.\n\nThe numerical variables need no preprocessing and, for the sake of simplicity,\nwe only try the default hyper-parameters for this model:\n\n"
+        "All is well. We are now ready to do some predictive modeling!\n\n## Gradient Boosting\n\nGradient Boosting Regression with decision trees is often flexible enough to\nefficiently handle heterogeneous tabular data with a mix of categorical and\nnumerical features as long as the number of samples is large enough.\n\nHere, we use the modern\n:class:`~sklearn.ensemble.HistGradientBoostingRegressor` with native support\nfor categorical features. Therefore, we only need to set\n`categorical_features=\"from_dtype\"` such that features with categorical dtype\nare considered categorical features. For reference, we extract the categorical\nfeatures from the dataframe based on the dtype. The internal trees use a dedicated\ntree splitting rule for these features.\n\nThe numerical variables need no preprocessing and, for the sake of simplicity,\nwe only try the default hyper-parameters for this model:\n\n"
       ]
     },
     {

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -165,7 +165,7 @@
 # -----------------
 #
 # Gradient Boosting Regression with decision trees is often flexible enough to
-# efficiently handle heterogenous tabular data with a mix of categorical and
+# efficiently handle heterogeneous tabular data with a mix of categorical and
 # numerical features as long as the number of samples is large enough.
 #
 # Here, we use the modern