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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(LinearSVC(dual="auto",...o", penalty="l1"))), ('classification', RandomForestClassifi())...scikit-learn.org/stable/_sources/modules/feature_selection.rst.txt -
index.rst.txt
Learning Curves and Checking Models' Scalability</div> </div> .. raw::...class="sphx-glr-thumbnail-title">Target Encoder's Internal Cross fitting</div>...scikit-learn.org/stable/_sources/auto_examples/index.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'** | +--------- | 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 -
about.rst.txt
core contributors to 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 -
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 -
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 -
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