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Results 21 - 30 of 37 for tests (0.07 sec)
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getting_started.rst.txt
X_test, y_train, y_test = train_test_split(X, y, random_state=0)...>>> X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)...scikit-learn.org/stable/_sources/getting_started.rst.txt -
preprocessing.rst.txt
X_test, y_train, y_test = train_test_split(X, y, random_state=42)...pipe.score(X_test, y_test) # apply scaling on testing data, without...scikit-learn.org/stable/_sources/modules/preprocessing.rst.txt -
plot_release_highlights_1_4_0.rst.txt
X_test, y_train, y_test = train_test_split(X_adult,...n(X_test) print(f"ROC AUC score is {roc_auc_score(y_test, y_decision)}")...scikit-learn.org/stable/_sources/auto_examples/release_highlights/plot_release_highlights_1_4_0.r... -
faq.rst.txt
^^^^^^^^^^ scikit-learn is regularly tested and maintained to work with...differently. Please refer to the test suite of the specific module...scikit-learn.org/stable/_sources/faq.rst.txt -
related_projects.rst.txt
facilitates best practices for testing and documenting estimators....More focused on statistical tests and less on prediction than...scikit-learn.org/stable/_sources/related_projects.rst.txt -
testimonials.rst.txt
but its careful and well tested implementation give us the...Moreover, its consistent API, well-tested code and permissive licensing...scikit-learn.org/stable/_sources/testimonials/testimonials.rst.txt -
grid_search.rst.txt
iter n_resources mean_test_score params ==== ====== ==========...done by using the :func:`train_test_split` utility function. Parallelism...scikit-learn.org/stable/_sources/modules/grid_search.rst.txt -
decomposition.rst.txt
To also transform a test set :math:`X`, we multiply it...scikit-learn.org/stable/_sources/modules/decomposition.rst.txt -
ensemble.rst.txt
train_test_split >>> X_train, X_test, y_train, y_test = train_test_split(X,...X_train, X_test = X[:2000], X[2000:] >>> y_train, y_test = y[:2000],...scikit-learn.org/stable/_sources/modules/ensemble.rst.txt -
neighbors.rst.txt
X_test, y_train, y_test = train_test_split(X, y, ......>>> print(nca_pipe.score(X_test, y_test)) 0.96190476... .. |nca_classification_1|...scikit-learn.org/stable/_sources/modules/neighbors.rst.txt