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model_evaluation.rst.txt
terms ''positive'' and ''negative'' refer to the classifier's prediction,...prediction, and the terms ''true'' and ''false'' refer to whether that...scikit-learn.org/stable/_sources/modules/model_evaluation.rst.txt -
cross_validation.rst.txt
keys()) ['estimator', 'fit_time', 'score_time', 'test_score'] Obtaining...sorted(scores.keys()) ['fit_time', 'score_time', 'test_precision_macro', 'test_recall_macro']...scikit-learn.org/stable/_sources/modules/cross_validation.rst.txt -
plot_discretization_strategies.rst.txt
strategy='uniform', strategy='quantile', strategy='kmeans' :srcset:...implemented in KBinsDiscretizer: - 'uniform': The discretization is uniform...scikit-learn.org/stable/_sources/auto_examples/preprocessing/plot_discretization_strategies.rst.txt -
feature_selection.rst.txt
: clf = Pipeline([ ('feature_selection', SelectFromModel(Lin...SVC(penalty="l1"))), ('classification', RandomForestClassifi())...scikit-learn.org/stable/_sources/modules/feature_selection.rst.txt -
plot_multi_metric_evaluation.rst.txt
in ``'_<scorer_name>'`` (``'mean_test_precision'``, ``'rank_test_precision'``,...make_scorer(accuracy_score)} # Setting refit='AUC', refits an estimator on the...scikit-learn.org/stable/_sources/auto_examples/model_selection/plot_multi_metric_evaluation.rst.txt -
linear_model.rst.txt
| **'lbfgs'** | **'liblinear'** | **'newton-cg'** | **'newton-cholesky'**...**'newton-cholesky'** | **'sag'** | **'saga'** | +--------- | L2 penalty...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 Team**...scikit-learn.org/stable/_sources/governance.rst.txt -
about.rst.txt
of consolidating scikit-learn's development and maintenance:...issues. Instead, please see `What's the best way to ask questions...scikit-learn.org/stable/_sources/about.rst.txt -
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 -
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