Pushing the docs to dev/ for branch: main, commit 0f43434e0c6906929dc2cfbd5e848e0d77df4f5e

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: 388ca4e58da662c2c7775f653cc4c463
+config: 6b45f82bc808975a706211de4fe3aebe
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -87,14 +87,14 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.decomposition import TruncatedSVD\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.ensemble import RandomTreesEmbedding\nfrom sklearn.manifold import (\n    MDS,\n    TSNE,\n    Isomap,\n    LocallyLinearEmbedding,\n    SpectralEmbedding,\n)\nfrom sklearn.neighbors import NeighborhoodComponentsAnalysis\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.random_projection import SparseRandomProjection\n\nembeddings = {\n    \"Random projection embedding\": SparseRandomProjection(\n        n_components=2, random_state=42\n    ),\n    \"Truncated SVD embedding\": TruncatedSVD(n_components=2),\n    \"Linear Discriminant Analysis embedding\": LinearDiscriminantAnalysis(\n        n_components=2\n    ),\n    \"Isomap embedding\": Isomap(n_neighbors=n_neighbors, n_components=2),\n    \"Standard LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"standard\"\n    ),\n    \"Modified LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"modified\"\n    ),\n    \"Hessian LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"hessian\"\n    ),\n    \"LTSA LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"ltsa\"\n    ),\n    \"MDS embedding\": MDS(\n        n_components=2, n_init=1, max_iter=120, n_jobs=2, normalized_stress=\"auto\"\n    ),\n    \"Random Trees embedding\": make_pipeline(\n        RandomTreesEmbedding(n_estimators=200, max_depth=5, random_state=0),\n        TruncatedSVD(n_components=2),\n    ),\n    \"Spectral embedding\": SpectralEmbedding(\n        n_components=2, random_state=0, eigen_solver=\"arpack\"\n    ),\n    \"t-SNE embeedding\": TSNE(\n        n_components=2,\n        n_iter=500,\n        n_iter_without_progress=150,\n        n_jobs=2,\n        random_state=0,\n    ),\n    \"NCA embedding\": NeighborhoodComponentsAnalysis(\n        n_components=2, init=\"pca\", random_state=0\n    ),\n}"
+        "from sklearn.decomposition import TruncatedSVD\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.ensemble import RandomTreesEmbedding\nfrom sklearn.manifold import (\n    MDS,\n    TSNE,\n    Isomap,\n    LocallyLinearEmbedding,\n    SpectralEmbedding,\n)\nfrom sklearn.neighbors import NeighborhoodComponentsAnalysis\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.random_projection import SparseRandomProjection\n\nembeddings = {\n    \"Random projection embedding\": SparseRandomProjection(\n        n_components=2, random_state=42\n    ),\n    \"Truncated SVD embedding\": TruncatedSVD(n_components=2),\n    \"Linear Discriminant Analysis embedding\": LinearDiscriminantAnalysis(\n        n_components=2\n    ),\n    \"Isomap embedding\": Isomap(n_neighbors=n_neighbors, n_components=2),\n    \"Standard LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"standard\"\n    ),\n    \"Modified LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"modified\"\n    ),\n    \"Hessian LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"hessian\"\n    ),\n    \"LTSA LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"ltsa\"\n    ),\n    \"MDS embedding\": MDS(\n        n_components=2, n_init=1, max_iter=120, n_jobs=2, normalized_stress=\"auto\"\n    ),\n    \"Random Trees embedding\": make_pipeline(\n        RandomTreesEmbedding(n_estimators=200, max_depth=5, random_state=0),\n        TruncatedSVD(n_components=2),\n    ),\n    \"Spectral embedding\": SpectralEmbedding(\n        n_components=2, random_state=0, eigen_solver=\"arpack\"\n    ),\n    \"t-SNE embedding\": TSNE(\n        n_components=2,\n        n_iter=500,\n        n_iter_without_progress=150,\n        n_jobs=2,\n        random_state=0,\n    ),\n    \"NCA embedding\": NeighborhoodComponentsAnalysis(\n        n_components=2, init=\"pca\", random_state=0\n    ),\n}"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Once we declared all the methodes of interest, we can run and perform the projection\nof the original data. We will store the projected data as well as the computational\ntime needed to perform each projection.\n\n"
+        "Once we declared all the methods of interest, we can run and perform the projection\nof the original data. We will store the projected data as well as the computational\ntime needed to perform each projection.\n\n"
       ]
     },
     {

dev/_downloads/1e0968da80ca868bbdf21c1d0547f68c/plot_lle_digits.ipynb

@@ -76,7 +76,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Training a :class:`~sklearn.svm.SVC` on a linear kernel results in an\nuntransformed feature space, where the hyperplane and the margins are\nstraight lines. Due to the lack of expressivity of the linear kernel, the\ntrained classes do not perfectly capture the training data.\n\n### Polynomial kernel\nThe polynomial kernel changes the notion of similarity. The kernel function\nis defined as:\n\n\\begin{align}K(\\mathbf{x}_1, \\mathbf{x}_2) = (\\gamma \\cdot \\\n      \\mathbf{x}_1^\\top\\mathbf{x}_2 + r)^d\\end{align}\n\nwhere ${d}$ is the degree (`degree`) of the polynomial, ${\\gamma}$\n(`gamma`) controls the influence of each individual training sample on the\ndecision boundary and ${r}$ is the bias term (`coef0`) that shifts the\ndata up or down. Here, we use the default value for the degree of the\npolynomal in the kernel funcion (`degree=3`). When `coef0=0` (the default),\nthe data is only transformed, but no additional dimension is added. Using a\npolynomial kernel is equivalent to creating\n:class:`~sklearn.preprocessing.PolynomialFeatures` and then fitting a\n:class:`~sklearn.svm.SVC` with a linear kernel on the transformed data,\nalthough this alternative approach would be computationally expensive for most\ndatasets.\n\n"
+        "Training a :class:`~sklearn.svm.SVC` on a linear kernel results in an\nuntransformed feature space, where the hyperplane and the margins are\nstraight lines. Due to the lack of expressivity of the linear kernel, the\ntrained classes do not perfectly capture the training data.\n\n### Polynomial kernel\nThe polynomial kernel changes the notion of similarity. The kernel function\nis defined as:\n\n\\begin{align}K(\\mathbf{x}_1, \\mathbf{x}_2) = (\\gamma \\cdot \\\n      \\mathbf{x}_1^\\top\\mathbf{x}_2 + r)^d\\end{align}\n\nwhere ${d}$ is the degree (`degree`) of the polynomial, ${\\gamma}$\n(`gamma`) controls the influence of each individual training sample on the\ndecision boundary and ${r}$ is the bias term (`coef0`) that shifts the\ndata up or down. Here, we use the default value for the degree of the\npolynomial in the kernel function (`degree=3`). When `coef0=0` (the default),\nthe data is only transformed, but no additional dimension is added. Using a\npolynomial kernel is equivalent to creating\n:class:`~sklearn.preprocessing.PolynomialFeatures` and then fitting a\n:class:`~sklearn.svm.SVC` with a linear kernel on the transformed data,\nalthough this alternative approach would be computationally expensive for most\ndatasets.\n\n"
       ]
     },
     {

dev/_downloads/264f6891fa2130246a013d5f089e7b2e/plot_svm_kernels.ipynb

@@ -112,7 +112,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## (Generalized) linear models\n\nWe start by modeling the target variable with the (l2 penalized) least\nsquares linear regression model, more comonly known as Ridge regression. We\nuse a low penalization `alpha`, as we expect such a linear model to under-fit\non such a large dataset.\n\n"
+        "## (Generalized) linear models\n\nWe start by modeling the target variable with the (l2 penalized) least\nsquares linear regression model, more commonly known as Ridge regression. We\nuse a low penalization `alpha`, as we expect such a linear model to under-fit\non such a large dataset.\n\n"
       ]
     },
     {

dev/_downloads/2e4791a177381a6102b21e44083615c8/plot_poisson_regression_non_normal_loss.ipynb

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Plot class probabilities calculated by the VotingClassifier\n\n.. currentmodule:: sklearn\n\nPlot the class probabilities of the first sample in a toy dataset predicted by\nthree different classifiers and averaged by the\n:class:`~ensemble.VotingClassifier`.\n\nFirst, three examplary classifiers are initialized\n(:class:`~linear_model.LogisticRegression`, :class:`~naive_bayes.GaussianNB`,\nand :class:`~ensemble.RandomForestClassifier`) and used to initialize a\nsoft-voting :class:`~ensemble.VotingClassifier` with weights `[1, 1, 5]`, which\nmeans that the predicted probabilities of the\n:class:`~ensemble.RandomForestClassifier` count 5 times as much as the weights\nof the other classifiers when the averaged probability is calculated.\n\nTo visualize the probability weighting, we fit each classifier on the training\nset and plot the predicted class probabilities for the first sample in this\nexample dataset.\n"
+        "\n# Plot class probabilities calculated by the VotingClassifier\n\n.. currentmodule:: sklearn\n\nPlot the class probabilities of the first sample in a toy dataset predicted by\nthree different classifiers and averaged by the\n:class:`~ensemble.VotingClassifier`.\n\nFirst, three exemplary classifiers are initialized\n(:class:`~linear_model.LogisticRegression`, :class:`~naive_bayes.GaussianNB`,\nand :class:`~ensemble.RandomForestClassifier`) and used to initialize a\nsoft-voting :class:`~ensemble.VotingClassifier` with weights `[1, 1, 5]`, which\nmeans that the predicted probabilities of the\n:class:`~ensemble.RandomForestClassifier` count 5 times as much as the weights\nof the other classifiers when the averaged probability is calculated.\n\nTo visualize the probability weighting, we fit each classifier on the training\nset and plot the predicted class probabilities for the first sample in this\nexample dataset.\n"
       ]
     },
     {

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -592,7 +592,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Those features are then combined with the ones already computed in the\nprevious spline-base pipeline. We can observe a nice performance improvemnt\nby modeling this pairwise interaction explicitly:\n\n"
+        "Those features are then combined with the ones already computed in the\nprevious spline-base pipeline. We can observe a nice performance improvement\nby modeling this pairwise interaction explicitly:\n\n"
       ]
     },
     {

dev/_downloads/7011de1f31ecdc52f138d7e582a6a455/plot_voting_probas.ipynb

@@ -184,7 +184,7 @@ def plot_training_data_with_decision_boundary(kernel):
 # (`gamma`) controls the influence of each individual training sample on the
 # decision boundary and :math:`{r}` is the bias term (`coef0`) that shifts the
 # data up or down. Here, we use the default value for the degree of the
-# polynomal in the kernel funcion (`degree=3`). When `coef0=0` (the default),
+# polynomial in the kernel function (`degree=3`). When `coef0=0` (the default),
 # the data is only transformed, but no additional dimension is added. Using a
 # polynomial kernel is equivalent to creating
 # :class:`~sklearn.preprocessing.PolynomialFeatures` and then fitting a

dev/_downloads/7012baed63b9a27f121bae611b8285c2/plot_cyclical_feature_engineering.ipynb

@@ -9,7 +9,7 @@
 three different classifiers and averaged by the
 :class:`~ensemble.VotingClassifier`.
 
-First, three examplary classifiers are initialized
+First, three exemplary classifiers are initialized
 (:class:`~linear_model.LogisticRegression`, :class:`~naive_bayes.GaussianNB`,
 and :class:`~ensemble.RandomForestClassifier`) and used to initialize a
 soft-voting :class:`~ensemble.VotingClassifier` with weights `[1, 1, 5]`, which