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plot_adaboost_regression.ipynb
\nregr_1.fit(X, y)\nregr_2.fit(X, y)\n\ny_1 = regr_1.predict(X)\ny_2...samples\")\nplt.plot(X, y_1, color=colors[1], label=\"n_estimators=1\", linew...scikit-learn.org/stable/_downloads/38e826c9e3778d7de78b2fc671fd7903/plot_adaboost_regression.ipynb -
plot_adaboost_regression.rst.txt
random_state=rng ) regr_1.fit(X, y) regr_2.fit(X, y) y_1 = regr_1.predict(X)...plt.plot(X, y_1, color=colors[1], label="n_estimators=1", linewidth=2)...scikit-learn.org/stable/_sources/auto_examples/ensemble/plot_adaboost_regression.rst.txt -
digg-favicon.png
interlaceMethod=none GRAY 8 8 16 1 1.0 whitePointX=31269, whitePointY=32899,...blueY=5999 Relative colorimetric 1 true true 16 Normal 16 2 UnsignedIntegral...cdn.digg.com/static/images/digg-favicon.png -
plot_classifier_comparison.rst.txt
C=1, random_state=42), GaussianProcessClass(1.0 * RBF(1.0),...max_features=1, random_state=42 ), MLPClassifier(alpha=1, max_iter=1000,...scikit-learn.org/stable/_sources/auto_examples/classification/plot_classifier_comparison.rst.txt -
plot_pca_iris.ipynb
1].mean() + 1.5,\n X[y == label, 2].mean(),\n...np.choose(y, [1, 2, 0]).astype(float)\nax.scatter(X[:, 0], X[:, 1], X[:,...scikit-learn.org/stable/_downloads/46b6a23d83637bf0f381ce9d8c528aa2/plot_pca_iris.ipynb -
plot_discretization_strategies.py
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/_downloads/43e84df0b93ff974da370e8da900f2ee/plot_discretization_strategie... -
plot_kmeans_digits.ipynb
1].min() - 1, reduced_data[:, 1].max() + 1\nxx, yy =...reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1\ny_min, y_max =...scikit-learn.org/stable/_downloads/6bf322ce1724c13e6e0f8f719ebd253c/plot_kmeans_digits.ipynb -
plot_classifier_comparison.ipynb
C=1, random_state=42),\n GaussianProcessClass(1.0 * RBF(1.0),...max_features=1, random_state=42\n ),\n MLPClassifier(alpha=1, max_iter=1000,...scikit-learn.org/stable/_downloads/3438aba177365cb595921cf18806dfa7/plot_classifier_comparison.ipynb -
plot_kmeans_digits.py
1].min() - 1, reduced_data[:, 1].max() + 1 xx, yy =...reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1 y_min, y_max =...scikit-learn.org/stable/_downloads/5a87b25ba023ee709595b8d02049f021/plot_kmeans_digits.py -
plot_classifier_comparison.py
C=1, random_state=42), GaussianProcessClass(1.0 * RBF(1.0),...max_features=1, random_state=42 ), MLPClassifier(alpha=1, max_iter=1000,...scikit-learn.org/stable/_downloads/2da0534ab0e0c8241033bcc2d912e419/plot_classifier_comparison.py
