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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 -
model_evaluation.rst.txt
ratio (pre-test and post-tests): .. math:: \text{post-test odds} =...\frac{\text{pre-test probability}}{1 - \text{pre-test probability}},...scikit-learn.org/stable/_sources/modules/model_evaluation.rst.txt -
index.rst.txt
example compares non-nested and nested cross-validation strategies...class="sphx-glr-thumbnail-title">Nested versus non-nested cross-validation</div>...scikit-learn.org/stable/_sources/auto_examples/index.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 -
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 -
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 -
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