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ensemble.rst.txt
to est >>> mean_squared_error(y_test, est.predict(X_test)) 3.84......train_test_split >>> X_train, X_test, y_train, y_test = train_test_split(X,...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 -
install.rst.txt
running the full scikit-learn test suite via automated continuous...ckages\\sklearn\\datasets\\tests\\data\\openml\\292\\api-v1-...scikit-learn.org/stable/_sources/install.rst.txt -
v1.3.rst.txt
and test curves by default. You can set `score_type="test"` to...`return_indices` to return the train-test indices of each cv split. :pr:`25659`...scikit-learn.org/stable/_sources/whats_new/v1.3.rst.txt -
related_projects.rst.txt
which 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 -
v1.4.rst.txt
train_test_split` in :pr:`26855` by `Tim...- :class:`feature_extraction.text.TfidfTransformer` in :pr:`27219`...scikit-learn.org/stable/_sources/whats_new/v1.4.rst.txt