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plot_multi_metric_evaluation.rst.txt
``'_<scorer_name>'`` (``'mean_test_precision'``, ``'rank_test_precision'``, etc...)...style in (("train", "--"), ("test", "-")): sample_score_mean =...scikit-learn.org/stable/_sources/auto_examples/model_selection/plot_multi_metric_evaluation.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 -
plot_release_highlights_1_4_0.rst.txt
X_test, y_train, y_test = train_test_split(X_adult,...--sklearn-color-text-on-default-background: var(--sg-text-color, va...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 -
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 -
clustering.rst.txt
math:: \text{ARI} = \frac{\text{RI} - E[\text{RI}]}{\max(\text{RI})...math:: \text{AMI} = \frac{\text{MI} - E[\text{MI}]}{\text{mean}(H(U),...scikit-learn.org/stable/_sources/modules/clustering.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 -
test.mm
1"> <node TEXT="Lorem ipsum. (ロ...raw.githubusercontent.com/codelibs/fess-testdata/master/xml/test.mm -
faq.rst.txt
:ref:`text_feature_extraction` for the built-in *text vectorizers*....in several ways. If you have text documents, you can use a term...scikit-learn.org/stable/_sources/faq.rst.txt -
plot_classifier_comparison.rst.txt
and test part X, y = ds X_train, X_test, y_train, y_test = train_test_split(...Plot the testing points ax.scatter( X_test[:, 0], X_test[:, 1],...scikit-learn.org/stable/_sources/auto_examples/classification/plot_classifier_comparison.rst.txt