Pushing the docs to dev/ for branch: main, commit 2fd022d972ab13f83bde581fa1f9ad74b4e6ebce

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: be7c284539ba9169136138383e7ead5b
+config: 8340a2641ea6cd48b5e6b1fa5e158d22
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -89,7 +89,7 @@
 # ------------
 #
 # Core samples (large dots) and non-core samples (small dots) are color-coded
-# according to the asigned cluster. Samples tagged as noise are represented in
+# according to the assigned cluster. Samples tagged as noise are represented in
 # black.
 
 unique_labels = set(labels)

dev/_downloads/0b802e24fdcd192a452e91580f278039/plot_dbscan.py

@@ -29,7 +29,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Define the classifiers\n\nHere we define two different classifiers. The goal is to visualy compare their\nstatistical performance across thresholds using the ROC and DET curves. There\nis no particular reason why these classifiers are chosen other classifiers\navailable in scikit-learn.\n\n"
+        "## Define the classifiers\n\nHere we define two different classifiers. The goal is to visually compare their\nstatistical performance across thresholds using the ROC and DET curves. There\nis no particular reason why these classifiers are chosen other classifiers\navailable in scikit-learn.\n\n"
       ]
     },
     {

dev/_downloads/10bb40e21b74618cdeed618ff1eae595/plot_det.ipynb

@@ -65,7 +65,7 @@
 
 # %%
 # Next we load the titanic dataset to demonstrate `set_output` with
-# :class:`compose.ColumnTransformer` and heterogenous data.
+# :class:`compose.ColumnTransformer` and heterogeneous data.
 from sklearn.datasets import fetch_openml
 
 X, y = fetch_openml(

dev/_downloads/1b3f17ff0f112d5b77cbdb90f1c17046/plot_set_output.py

@@ -122,7 +122,7 @@
 # The previous figure compares the learned model of KRR and SVR when both
 # complexity/regularization and bandwidth of the RBF kernel are optimized using
 # grid-search. The learned functions are very similar; however, fitting KRR is
-# approximatively 3-4 times faster than fitting SVR (both with grid-search).
+# approximately 3-4 times faster than fitting SVR (both with grid-search).
 #
 # Prediction of 100000 target values could be in theory approximately three
 # times faster with SVR since it has learned a sparse model using only

dev/_downloads/1dcd684ce26b8c407ec2c2d2101c5c73/plot_kernel_ridge_regression.py

@@ -94,7 +94,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\n\nfig = plt.figure(figsize=(8, 3))\nfig.subplots_adjust(left=0.02, right=0.98, bottom=0.05, top=0.9)\ncolors = [\"#4EACC5\", \"#FF9C34\", \"#4E9A06\"]\n\n# KMeans\nax = fig.add_subplot(1, 3, 1)\nfor k, col in zip(range(n_clusters), colors):\n    my_members = k_means_labels == k\n    cluster_center = k_means_cluster_centers[k]\n    ax.plot(X[my_members, 0], X[my_members, 1], \"w\", markerfacecolor=col, marker=\".\")\n    ax.plot(\n        cluster_center[0],\n        cluster_center[1],\n        \"o\",\n        markerfacecolor=col,\n        markeredgecolor=\"k\",\n        markersize=6,\n    )\nax.set_title(\"KMeans\")\nax.set_xticks(())\nax.set_yticks(())\nplt.text(-3.5, 1.8, \"train time: %.2fs\\ninertia: %f\" % (t_batch, k_means.inertia_))\n\n# MiniBatchKMeans\nax = fig.add_subplot(1, 3, 2)\nfor k, col in zip(range(n_clusters), colors):\n    my_members = mbk_means_labels == k\n    cluster_center = mbk_means_cluster_centers[k]\n    ax.plot(X[my_members, 0], X[my_members, 1], \"w\", markerfacecolor=col, marker=\".\")\n    ax.plot(\n        cluster_center[0],\n        cluster_center[1],\n        \"o\",\n        markerfacecolor=col,\n        markeredgecolor=\"k\",\n        markersize=6,\n    )\nax.set_title(\"MiniBatchKMeans\")\nax.set_xticks(())\nax.set_yticks(())\nplt.text(-3.5, 1.8, \"train time: %.2fs\\ninertia: %f\" % (t_mini_batch, mbk.inertia_))\n\n# Initialize the different array to all False\ndifferent = mbk_means_labels == 4\nax = fig.add_subplot(1, 3, 3)\n\nfor k in range(n_clusters):\n    different += (k_means_labels == k) != (mbk_means_labels == k)\n\nidentic = np.logical_not(different)\nax.plot(X[identic, 0], X[identic, 1], \"w\", markerfacecolor=\"#bbbbbb\", marker=\".\")\nax.plot(X[different, 0], X[different, 1], \"w\", markerfacecolor=\"m\", marker=\".\")\nax.set_title(\"Difference\")\nax.set_xticks(())\nax.set_yticks(())\n\nplt.show()"
+        "import matplotlib.pyplot as plt\n\nfig = plt.figure(figsize=(8, 3))\nfig.subplots_adjust(left=0.02, right=0.98, bottom=0.05, top=0.9)\ncolors = [\"#4EACC5\", \"#FF9C34\", \"#4E9A06\"]\n\n# KMeans\nax = fig.add_subplot(1, 3, 1)\nfor k, col in zip(range(n_clusters), colors):\n    my_members = k_means_labels == k\n    cluster_center = k_means_cluster_centers[k]\n    ax.plot(X[my_members, 0], X[my_members, 1], \"w\", markerfacecolor=col, marker=\".\")\n    ax.plot(\n        cluster_center[0],\n        cluster_center[1],\n        \"o\",\n        markerfacecolor=col,\n        markeredgecolor=\"k\",\n        markersize=6,\n    )\nax.set_title(\"KMeans\")\nax.set_xticks(())\nax.set_yticks(())\nplt.text(-3.5, 1.8, \"train time: %.2fs\\ninertia: %f\" % (t_batch, k_means.inertia_))\n\n# MiniBatchKMeans\nax = fig.add_subplot(1, 3, 2)\nfor k, col in zip(range(n_clusters), colors):\n    my_members = mbk_means_labels == k\n    cluster_center = mbk_means_cluster_centers[k]\n    ax.plot(X[my_members, 0], X[my_members, 1], \"w\", markerfacecolor=col, marker=\".\")\n    ax.plot(\n        cluster_center[0],\n        cluster_center[1],\n        \"o\",\n        markerfacecolor=col,\n        markeredgecolor=\"k\",\n        markersize=6,\n    )\nax.set_title(\"MiniBatchKMeans\")\nax.set_xticks(())\nax.set_yticks(())\nplt.text(-3.5, 1.8, \"train time: %.2fs\\ninertia: %f\" % (t_mini_batch, mbk.inertia_))\n\n# Initialize the different array to all False\ndifferent = mbk_means_labels == 4\nax = fig.add_subplot(1, 3, 3)\n\nfor k in range(n_clusters):\n    different += (k_means_labels == k) != (mbk_means_labels == k)\n\nidentical = np.logical_not(different)\nax.plot(X[identical, 0], X[identical, 1], \"w\", markerfacecolor=\"#bbbbbb\", marker=\".\")\nax.plot(X[different, 0], X[different, 1], \"w\", markerfacecolor=\"m\", marker=\".\")\nax.set_title(\"Difference\")\nax.set_xticks(())\nax.set_yticks(())\n\nplt.show()"
       ]
     }
   ],

dev/_downloads/1f948ff6f5face5a362672c4e36dd01e/plot_mini_batch_kmeans.ipynb

@@ -83,7 +83,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n## Using feature names to specify monotonic constraints\n\nNote that if the training data has feature names, it's possible to specifiy the\nmonotonic constraints by passing a dictionary:\n\n"
+        "\n## Using feature names to specify monotonic constraints\n\nNote that if the training data has feature names, it's possible to specify the\nmonotonic constraints by passing a dictionary:\n\n"
       ]
     },
     {

dev/_downloads/215c560d29193ab9b0a495609bc74802/plot_monotonic_constraints.ipynb

@@ -83,7 +83,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Plot results\n\nCore samples (large dots) and non-core samples (small dots) are color-coded\naccording to the asigned cluster. Samples tagged as noise are represented in\nblack.\n\n"
+        "## Plot results\n\nCore samples (large dots) and non-core samples (small dots) are color-coded\naccording to the assigned cluster. Samples tagged as noise are represented in\nblack.\n\n"
       ]
     },
     {

dev/_downloads/23a5a575578f08cc0071cc070953a655/plot_dbscan.ipynb

@@ -132,8 +132,8 @@
 for k in range(n_clusters):
     different += (k_means_labels == k) != (mbk_means_labels == k)
 
-identic = np.logical_not(different)
-ax.plot(X[identic, 0], X[identic, 1], "w", markerfacecolor="#bbbbbb", marker=".")
+identical = np.logical_not(different)
+ax.plot(X[identical, 0], X[identical, 1], "w", markerfacecolor="#bbbbbb", marker=".")
 ax.plot(X[different, 0], X[different, 1], "w", markerfacecolor="m", marker=".")
 ax.set_title("Difference")
 ax.set_xticks(())