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about.rst.txt
please see `What's the best way to ask questions about scikit-learn.../stable/faq.html#what-s-the-best-way-to-get-help-on-scikit-learn-usage>`_...scikit-learn.org/stable/_sources/about.rst.txt -
support.rst.txt
about repository updates and test failures on the `scikit-learn-commits...scikit-learn.org/stable/_sources/support.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 -
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... -
classes.rst.txt
_text_feature_extraction_ref: From text --------- .....feature_extraction.text.CountVectorizer feature_extraction.text.HashingVectorizer...scikit-learn.org/stable/_sources/modules/classes.rst.txt -
feature_extraction.rst.txt
:math:`\text{tf-idf}_{\text{term1}} = \text{tf} \times \text{idf}...:math:`\text{tf-idf(t,d)}=\text{tf(t,d)} \times \text{idf(t)}`....scikit-learn.org/stable/_sources/modules/feature_extraction.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 -
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 -
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