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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 -
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 -
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... -
decomposition.rst.txt
||X-UV||_{\text{Fro}}^2+\alpha||V||_{1,1} \\ \text{subject to...||X-UV||_{\text{Fro}}^2+\alpha||U||_{1,1} \\ \text{subject to...scikit-learn.org/stable/_sources/modules/decomposition.rst.txt -
grid_search.rst.txt
have identified the best candidate. The best candidate is identified...`factor=2` candidates: the best candidate is the best out of these 2 candidates....scikit-learn.org/stable/_sources/modules/grid_search.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 -
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