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cross_validation.rst.txt
available data as a **test set** ``X_test, y_test``. Note that the...>>> X_train, X_test, y_train, y_test = train_test_split( ... X,...scikit-learn.org/stable/_sources/modules/cross_validation.rst.txt -
feature_selection.rst.txt
the best features based on univariate statistical tests. It can...we can use a F-test to retrieve the two best features for a dataset...scikit-learn.org/stable/_sources/modules/feature_selection.rst.txt -
linear_model.rst.txt
cost of :math:`O(n_{\text{samples}} n_{\text{features}}^2)`, assuming...that :math:`n_{\text{samples}} \geq n_{\text{features}}`. .....scikit-learn.org/stable/_sources/modules/linear_model.rst.txt -
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
K_{test} - 1'_{\text{n}_{samples}} K - K_{test} 1_{\text{n}_{samples}}...>>> X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)...scikit-learn.org/stable/_sources/modules/preprocessing.rst.txt -
glossary.rst.txt
common tests This refers to the tests run on almost every...``(train_idx, test_idx)`` pairs. Each of {train,test}_idx is a 1d...scikit-learn.org/stable/_sources/glossary.rst.txt -
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 -
faq.rst.txt
:ref:`text_feature_extraction` for the built-in *text vectorizers*....^^^^^^^^^^ scikit-learn is regularly tested and maintained to work with...scikit-learn.org/stable/_sources/faq.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 -
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...