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feature_extraction.rst.txt
() array([' w', 'ds', 'or', 'pr', 'rd', 's ', 'wo', 'wp'], ...)...array(['and', 'document', 'first', 'is', 'one', 'second', 'the',...scikit-learn.org/stable/_sources/modules/feature_extraction.rst.txt -
contributing.rst.txt
brackets: ``input: {'log', 'squared', 'multinomial'}`` * 1D or 2D data...it-learn>`__: click on the 'Fork' button near the top of the...scikit-learn.org/dev/_sources/developers/contributing.rst.txt -
support.rst.txt
Older versions' printable PDF documentation is...scikit-learn.org/stable/_sources/support.rst.txt -
classes.rst.txt
SGDRegressor` with ``loss='huber'``. .. autosummary:: :toctree:...scikit-learn.org/stable/_sources/modules/classes.rst.txt -
preprocessing.rst.txt
['female', 'Europe', 'Firefox'], ... ['female', 'Asia', 'Chrome']]...X = [['male', 'from US', 'uses Safari'], ['female', 'from Europe',...scikit-learn.org/stable/_sources/modules/preprocessing.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 -
testimonials.rst.txt
I think it's the most well-designed ML package I've seen so far....in the lead's tenure on our site. Also, our users' profiles consist...scikit-learn.org/stable/_sources/testimonials/testimonials.rst.txt -
decomposition.rst.txt
Descent ('cd') [5]_, and Multiplicative Update ('mu') [6]_. The...optional parameter ``svd_solver='randomized'`` is very useful in that...scikit-learn.org/stable/_sources/modules/decomposition.rst.txt