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feature_extraction.rst.txt
array([[1, 1, 1, 0, 1, 1, 1, 0], [1, 1, 0, 1, 1, 1, 0, 1]]) In...array([[0, 1, 1, 1, 0, 0, 1, 0, 1], [0, 1, 0, 1, 0, 2, 1, 0, 1], [1,...scikit-learn.org/stable/_sources/modules/feature_extraction.rst.txt -
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 -
user_guide.rst.txt
toctree:: :numbered: :maxdepth: 1 metadata_routing.rst...scikit-learn.org/stable/_sources/user_guide.rst.txt -
preprocessing.rst.txt
1. , 0.1], [4.4, 2.2, 1.1, 0.1], [4.4, 2.2, 1.2, 0.1], ...,...array([[1., 0., 0., 1., 0., 1.], [0., 1., 1., 0., 0., 1.]]) By...scikit-learn.org/stable/_sources/modules/preprocessing.rst.txt -
clustering.rst.txt
1, 1, 1] >>> labels_pred = [0, 0, 1, 1, 2, 2] >>>...= [0, 0, 0, 1, 1, 1] >>> labels_pred = [0, 0, 1, 1, 2, 2] >>>...scikit-learn.org/stable/_sources/modules/clustering.rst.txt -
getting_started.rst.txt
dataset is easy array([1., 1., 1., 1., 1.]) Automatic parameter...ransform(X) array([[-1., 1.], [ 1., -1.]]) Sometimes, you want...scikit-learn.org/stable/_sources/getting_started.rst.txt -
decomposition.rst.txt
array([[1, 1], [2, 1], [3, 1.2], [4, 1], [5, 0.8], [6, 1]]) >>>...np.array([[1, 0], [1, 6.1], [1, 0], [1, 4], [3.2, 1], [0, 4]])...scikit-learn.org/stable/_sources/modules/decomposition.rst.txt -
grid_search.rst.txt
{'C': [1, 10, 100, 1000], 'kernel': ['linear']}, {'C': [1, 10,...``loguniform(1, 100)`` can be used instead of ``[1, 10, 100]``....scikit-learn.org/stable/_sources/modules/grid_search.rst.txt -
glossary.rst.txt
``[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}]`` instead...instead of ``[{1:1}, {2:5}, {3:1}, {4:1}]``. The ``class_weight``...scikit-learn.org/stable/_sources/glossary.rst.txt -
test.mm
1"> <node TEXT="Lorem ipsum. (ロ...raw.githubusercontent.com/codelibs/fess-testdata/master/xml/test.mm