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linear_model.rst.txt
| **'lbfgs'** | **'liblinear'** | **'newton-cg'** | **'newton-cholesky'**...**'newton-cholesky'** | **'sag'** | **'saga'** | +--------- | Multinomial...scikit-learn.org/stable/_sources/modules/linear_model.rst.txt -
governance.rst.txt
that takes place on the project's private mailing list. While it...pull requests in scikit-learn's repository. Maintainers ~~~~~~~~~~...scikit-learn.org/stable/_sources/governance.rst.txt -
feature_selection.rst.txt
: clf = Pipeline([ ('feature_selection', SelectFromModel(LinearSVC(dual="auto",...o", penalty="l1"))), ('classification', RandomForestClassifi())...scikit-learn.org/stable/_sources/modules/feature_selection.rst.txt -
classes.rst.txt
SGDRegressor` with ``loss='huber'``. .. autosummary:: :toctree:...scikit-learn.org/stable/_sources/modules/classes.rst.txt -
grid_search.rst.txt
= [ {'C': [1, 10, 100, 1000], 'kernel': ['linear']}, {'C': [1,...e=.1), 'kernel': ['rbf'], 'class_weight':['balanced', None]}...scikit-learn.org/stable/_sources/modules/grid_search.rst.txt -
glossary.rst.txt
of ['no', 'yes'], 'yes' is the positive class; of ['no', 'YES'],...... param_grid={'loss': ['log_loss', 'hinge']}) This means that...scikit-learn.org/stable/_sources/glossary.rst.txt -
getting_started.rst.txt
param_distributions = {'n_estimators': randint(1, 5), ... 'max_depth': randint(5,...param_distributions={'max_depth': ..., 'n_estimators': ...}, random_state=0)...scikit-learn.org/stable/_sources/getting_started.rst.txt -
clustering.rst.txt
Their 'sqrt' and 'sum' averages are the geometric...s(i, k) - max [ a(i, k') + s(i, k') \forall k' \neq k ] Where :math:`s(i,...scikit-learn.org/stable/_sources/modules/clustering.rst.txt -
faq.rst.txt
need to specify algorithm='brute' as the default assumes >>>...eps=5, min_samples=2, algorithm='brute') # doctest: +SKIP (array([0,...scikit-learn.org/stable/_sources/faq.rst.txt -
roadmap.rst.txt
right now it's impossible to clone a `CalibratedClassifier`...scikit-learn.org/stable/_sources/roadmap.rst.txt