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linear_model.rst.txt
``C`` is given by ``alpha = 1 / C`` or ``alpha =...:func:`sklearn.svm.l1_min_c` allows to calculate the lower bound for C in order...scikit-learn.org/stable/_sources/modules/linear_model.rst.txt -
cross_validation.rst.txt
"c", "c", "c", "d", "d", "d"] >>> groups...= ["a", "b", "b", "b", "c", "c", "c", "a"] >>> groups = [1, 1,...scikit-learn.org/stable/_sources/modules/cross_validation.rst.txt -
feature_selection.rst.txt
the parameter C controls the sparsity: the smaller C the fewer features...(150, 4) >>> lsvc = LinearSVC(C=0.01, penalty="l1", dual=False).fit(X,...scikit-learn.org/stable/_sources/modules/feature_selection.rst.txt -
clustering.rst.txt
\sum_{q=1}^k n_q (c_q - c_E) (c_q - c_E)^T with :math:`C_q` the set...math:: C = \left[\begin{matrix} C_{00} & C_{01} \\ C_{10} & C_{11}...scikit-learn.org/stable/_sources/modules/clustering.rst.txt -
grid_search.rst.txt
= [ {'C': [1, 10, 100, 1000], 'kernel': ['linear']}, {'C': [1,...classes. Typical examples include ``C``, ``kernel`` and ``gamma`` for...scikit-learn.org/stable/_sources/modules/grid_search.rst.txt -
faq.rst.txt
favor different data layouts (`X` C-contiguous or F-contiguous, sparse...with OpenMP (for code written in C or Cython), - scikit-learn relies...scikit-learn.org/stable/_sources/faq.rst.txt -
install.rst.txt
for instance:: C:\Users\username>C:\Users\username\AppD...><span>conda create -n sklearn-env -c conda-forge scikit-learn</span>...scikit-learn.org/stable/_sources/install.rst.txt -
testimonials.rst.txt
commercial and marketing teams called C-Radar. We extensively use scikit-learn...scikit-learn.org/stable/_sources/testimonials/testimonials.rst.txt -
plot_release_highlights_1_4_0.rst.txt
or with conda:: conda install -c conda-forge scikit-learn .. GENERATED...scikit-learn.org/stable/_sources/auto_examples/release_highlights/plot_release_highlights_1_4_0.r... -
classes.rst.txt
l1_min_c .. _tree_ref: :mod:`sklearn.tree`:...scikit-learn.org/stable/_sources/modules/classes.rst.txt