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Pushing the docs to dev/ for branch: main, commit dbde1da1954be91b9a0a12c9a109c17cd109bc76
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‎dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

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‎dev/_downloads/521b554adefca348463adbbe047d7e99/plot_linear_model_coefficient_interpretation.py

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# %%
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plt.figure(figsize=(9, 7))
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sns.stripplot(data=coefs, orient="h", color="k", alpha=0.5)
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sns.stripplot(data=coefs, orient="h", palette="dark:k", alpha=0.5)
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sns.boxplot(data=coefs, orient="h", color="cyan", saturation=0.5, whis=10)
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plt.axvline(x=0, color=".5")
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plt.xlabel("Coefficient importance")
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# %%
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plt.figure(figsize=(9, 7))
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sns.stripplot(data=coefs, orient="h", color="k", alpha=0.5)
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sns.stripplot(data=coefs, orient="h", palette="dark:k", alpha=0.5)
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sns.boxplot(data=coefs, orient="h", color="cyan", saturation=0.5)
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plt.axvline(x=0, color=".5")
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plt.title("Coefficient importance and its variability")
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# %%
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plt.figure(figsize=(9, 7))
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sns.stripplot(data=coefs, orient="h", color="k", alpha=0.5)
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sns.stripplot(data=coefs, orient="h", palette="dark:k", alpha=0.5)
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sns.boxplot(data=coefs, orient="h", color="cyan", saturation=0.5, whis=10)
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plt.axvline(x=0, color=".5")
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plt.title("Coefficient variability")
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# %%
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plt.figure(figsize=(9, 7))
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sns.stripplot(data=coefs, orient="h", color="k", alpha=0.5)
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sns.stripplot(data=coefs, orient="h", palette="dark:k", alpha=0.5)
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sns.boxplot(data=coefs, orient="h", color="cyan", saturation=0.5, whis=100)
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plt.axvline(x=0, color=".5")
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plt.title("Coefficient variability")

‎dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

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‎dev/_downloads/c8db473878b6afea8e75e36dc828f109/plot_compare_methods.ipynb

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},
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"outputs": [],
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"source": [
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"md_scaling = manifold.MDS(\n n_components=n_components, max_iter=50, n_init=4, random_state=0\n)\nS_scaling = md_scaling.fit_transform(S_points)\n\nplot_2d(S_scaling, S_color, \"Multidimensional scaling\")"
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"md_scaling = manifold.MDS(\n n_components=n_components,\n max_iter=50,\n n_init=4,\n random_state=0,\n normalized_stress=False,\n)\nS_scaling = md_scaling.fit_transform(S_points)\n\nplot_2d(S_scaling, S_color, \"Multidimensional scaling\")"
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]
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},
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{

‎dev/_downloads/cda53b33015268619bc212d32b7000b9/plot_compare_methods.py

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# Read more in the :ref:`User Guide <multidimensional_scaling>`.
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md_scaling = manifold.MDS(
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n_components=n_components, max_iter=50, n_init=4, random_state=0
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n_components=n_components,
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max_iter=50,
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n_init=4,
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random_state=0,
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normalized_stress=False,
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)
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S_scaling = md_scaling.fit_transform(S_points)
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‎dev/_downloads/cf0f90f46eb559facf7f63f124f61e04/plot_linear_model_coefficient_interpretation.ipynb

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},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(9, 7))\nsns.stripplot(data=coefs, orient=\"h\", color=\"k\", alpha=0.5)\nsns.boxplot(data=coefs, orient=\"h\", color=\"cyan\", saturation=0.5, whis=10)\nplt.axvline(x=0, color=\".5\")\nplt.xlabel(\"Coefficient importance\")\nplt.title(\"Coefficient importance and its variability\")\nplt.suptitle(\"Ridge model, small regularization\")\nplt.subplots_adjust(left=0.3)"
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"plt.figure(figsize=(9, 7))\nsns.stripplot(data=coefs, orient=\"h\", palette=\"dark:k\", alpha=0.5)\nsns.boxplot(data=coefs, orient=\"h\", color=\"cyan\", saturation=0.5, whis=10)\nplt.axvline(x=0, color=\".5\")\nplt.xlabel(\"Coefficient importance\")\nplt.title(\"Coefficient importance and its variability\")\nplt.suptitle(\"Ridge model, small regularization\")\nplt.subplots_adjust(left=0.3)"
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]
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},
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{
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},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(9, 7))\nsns.stripplot(data=coefs, orient=\"h\", color=\"k\", alpha=0.5)\nsns.boxplot(data=coefs, orient=\"h\", color=\"cyan\", saturation=0.5)\nplt.axvline(x=0, color=\".5\")\nplt.title(\"Coefficient importance and its variability\")\nplt.xlabel(\"Coefficient importance\")\nplt.suptitle(\"Ridge model, small regularization, AGE dropped\")\nplt.subplots_adjust(left=0.3)"
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"plt.figure(figsize=(9, 7))\nsns.stripplot(data=coefs, orient=\"h\", palette=\"dark:k\", alpha=0.5)\nsns.boxplot(data=coefs, orient=\"h\", color=\"cyan\", saturation=0.5)\nplt.axvline(x=0, color=\".5\")\nplt.title(\"Coefficient importance and its variability\")\nplt.xlabel(\"Coefficient importance\")\nplt.suptitle(\"Ridge model, small regularization, AGE dropped\")\nplt.subplots_adjust(left=0.3)"
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]
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},
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{
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},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(9, 7))\nsns.stripplot(data=coefs, orient=\"h\", color=\"k\", alpha=0.5)\nsns.boxplot(data=coefs, orient=\"h\", color=\"cyan\", saturation=0.5, whis=10)\nplt.axvline(x=0, color=\".5\")\nplt.title(\"Coefficient variability\")\nplt.subplots_adjust(left=0.3)"
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"plt.figure(figsize=(9, 7))\nsns.stripplot(data=coefs, orient=\"h\", palette=\"dark:k\", alpha=0.5)\nsns.boxplot(data=coefs, orient=\"h\", color=\"cyan\", saturation=0.5, whis=10)\nplt.axvline(x=0, color=\".5\")\nplt.title(\"Coefficient variability\")\nplt.subplots_adjust(left=0.3)"
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]
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},
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{
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},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(9, 7))\nsns.stripplot(data=coefs, orient=\"h\", color=\"k\", alpha=0.5)\nsns.boxplot(data=coefs, orient=\"h\", color=\"cyan\", saturation=0.5, whis=100)\nplt.axvline(x=0, color=\".5\")\nplt.title(\"Coefficient variability\")\nplt.subplots_adjust(left=0.3)"
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"plt.figure(figsize=(9, 7))\nsns.stripplot(data=coefs, orient=\"h\", palette=\"dark:k\", alpha=0.5)\nsns.boxplot(data=coefs, orient=\"h\", color=\"cyan\", saturation=0.5, whis=100)\nplt.axvline(x=0, color=\".5\")\nplt.title(\"Coefficient variability\")\nplt.subplots_adjust(left=0.3)"
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]
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},
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{

‎dev/_downloads/scikit-learn-docs.zip

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‎dev/_images/sphx_glr_plot_agglomerative_clustering_002.png

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‎dev/_images/sphx_glr_plot_agglomerative_clustering_003.png

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‎dev/_images/sphx_glr_plot_agglomerative_clustering_004.png

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