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linear_model.rst.txt
array([[1, 0, 0, 0], [1, 0, 1, 0], [1, 1, 0, 0], [1, 1, 1, 1]]) >>>...1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03,...scikit-learn.org/stable/_sources/modules/linear_model.rst.txt -
about.rst.txt
btn-warning btn-big sk-donate-btn mb-1" href="https://numfocus.org/d...scikit-learn.org/stable/_sources/about.rst.txt -
plot_release_highlights_1_4_0.rst.txt
1, 6, np.nan]).reshape(-1, 1) y = [0, 0, 1, 1] forest...<td>1.0</td><td>0.0</td></tr><tr><td>-1.293993</td><td>1.0</...scikit-learn.org/stable/_sources/auto_examples/release_highlights/plot_release_highlights_1_4_0.r... -
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 -
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 -
roadmap.rst.txt
cross-version safety from version 1.0. Note: Gael and Olivier think...scikit-learn.org/stable/_sources/roadmap.rst.txt