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plot_multi_metric_evaluation.py
1) # Get the regular numpy array...sample_score_mean + sample_score_std, alpha=0.1 if sample == "test" else 0, color=color,...scikit-learn.org/stable/_downloads/dedbcc9464f3269f4f012f4bfc7d16da/plot_multi_metric_evaluation.py -
pydata-sphinx-theme.js
u={">":[1],">=":[0,1],"=":[0],"<=":[-1,0],"<":[-1]},m=Objec...("."),a.split(".")):o||a?o?-1:1:0})(e,t);return u[n].include...scikit-learn.org/dev/_static/scripts/pydata-sphinx-theme.js -
plot_discretization_strategies.rst.txt
8]]) centers_1 = np.array([[0, 0], [3, 1]]) # construct the...len(strategies) + 1, i) ax.scatter(X[:, 0], X[:, 1], edgecolors="k")...scikit-learn.org/stable/_sources/auto_examples/preprocessing/plot_discretization_strategies.rst.txt -
index.css
padding-left: 1.3rem !important; padding-right: 1.3rem !important;...html[data-theme="light"] { --sk-landing-bg-1: var(--sk-cyan-shades-3); --sk-landing-bg-2:...scikit-learn.org/stable/_static/styles/index.css -
plot_pca_iris.py
1].mean() + 1.5, X[y == label, 2].mean(),...np.choose(y, [1, 2, 0]).astype(float) ax.scatter(X[:, 0], X[:, 1], X[:,...scikit-learn.org/stable/_downloads/1168f82083b3e70f31672e7c33738f8d/plot_pca_iris.py -
colors.css
--sk-cyan-tint-1: #4bb4e5; --sk-cyan: #29abe2; --sk-cyan-shades-1: #2294c4;...--sk-orange-tint-1: #f99f44; --sk-orange: #f7931e; --sk-orange-shades-1: #d77f19;...scikit-learn.org/stable/_static/styles/colors.css -
jupyterlite_sphinx.css
jupyterlite_sphinx_iframe { z-index: 1; position: relative; border-style:...translateY(-50%) translateX(-50%) scale(1.2); } .jupyterlite_sphinx_try_it_button_clicked...scikit-learn.org/stable/_static/jupyterlite_sphinx.css -
sg_gallery.css
sphinx themes Tested for Sphinx 1.3.1 for all themes: default, alabaster,...--sg-download-a-hover-box-shadow-1: rgba(255, 255, 255, 0.1); --sg-download-...scikit-learn.org/stable/_static/sg_gallery.css -
plot_multi_metric_evaluation.rst.txt
1) # Get the regular numpy array...sample_score_mean + sample_score_std, alpha=0.1 if sample == "test" else 0, color=color,...scikit-learn.org/stable/_sources/auto_examples/model_selection/plot_multi_metric_evaluation.rst.txt -
plot_multi_metric_evaluation.ipynb
1)\n\n# Get the regular numpy array...+ sample_score_std,\n alpha=0.1 if sample == \"test\" else 0,\n...scikit-learn.org/stable/_downloads/f57e1ee55d4c7a51949d5c26b3af07bb/plot_multi_metric_evaluation....