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index.rst.txt
_tutorial_menu: ========== scikit-learn Tutorials ========== .. toctree::...scikit-learn.org/stable/_sources/tutorial/index.rst.txt -
plot_discretization_strategies.ipynb
cluster_std=0.5,\n centers=centers_0,\n random_state=random_state,\n...)[0],\n]\n\nfigure = plt.figure(figsize=(14, 9))\ni = 1\nfor ds_cnt,...scikit-learn.org/stable/_downloads/adc9be3b7acc279025dad9ee4ce92038/plot_discretization_strategie... -
plot_classifier_comparison.py
""" ========== Classifier comparison ========== A comparison...random_state=42), SVC(gamma=2, C=1, random_state=42), GaussianProcessClass(1.0...scikit-learn.org/stable/_downloads/2da0534ab0e0c8241033bcc2d912e419/plot_classifier_comparison.py -
digg-favicon.png
PixelInterleaved width=16, height=16, bitDepth=8, colorType=GrayAlpha, ...whitePointX=31269, whitePointY=32899, redX=63999, redY=33001, greenX=30000,...cdn.digg.com/static/images/digg-favicon.png -
plot_pca_iris.py
""" ========== PCA example with Iris Data-set ========== Principal...2)]: ax.text3D( X[y == label, 0].mean(), X[y == label, 1].mean()...scikit-learn.org/stable/_downloads/1168f82083b3e70f31672e7c33738f8d/plot_pca_iris.py -
plot_discretization_strategies.rst.txt
py: ========== Demonstrating the different...strategies of KBinsDiscretizer ========== This example presents the...scikit-learn.org/stable/_sources/auto_examples/preprocessing/plot_discretization_strategies.rst.txt -
digg-logo-512.png
blue=0 index=21, red=0, green=0, blue=0 index=22, red=0, green=0,...red=0, green=0, blue=0 index=27, red=0, green=0, blue=0 index=28,...cdn.digg.com/static/images/digg-logo-512.png -
plot_kmeans_digits.ipynb
metrics):\n\n========== ==========nShorthand full name\n========== ==========nhomo...silhouette coefficient\n========== ==========n" ] }, { "cell_type":...scikit-learn.org/stable/_downloads/6bf322ce1724c13e6e0f8f719ebd253c/plot_kmeans_digits.ipynb -
plot_multi_metric_evaluation.ipynb
\n alpha=0.1 if sample == \"test\" else 0,\n color=color,\n )\n... style,\n color=color,\n alpha=1 if sample == \"test\" else 0.7,\n...scikit-learn.org/stable/_downloads/f57e1ee55d4c7a51949d5c26b3af07bb/plot_multi_metric_evaluation.... -
plot_adaboost_regression.ipynb
color=colors[1], label=\"n_estimators=1\", linewidth=2)\nplt.plot(X,...color=colors[2], label=\"n_estimators=300\", linewidth=2)\np...scikit-learn.org/stable/_downloads/38e826c9e3778d7de78b2fc671fd7903/plot_adaboost_regression.ipynb
