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Comparison of F-test and mutual information — s...
univariate F-tests statistics and mutual information. As F-test captures...( 1000 ) f_test , _ = f_regression ( X , y ) f_test /= np . max...scikit-learn.org/stable/auto_examples/feature_selection/plot_f_test_vs_mi.html -
ワイルドカード検索
test * 上記の場合は、test、testsやtesterなど、複数文字のワイルドカードとして扱われます。また、...うに ? を利用します。 te?t 上記の場合は、textやtestなど、1 文字のワイルドカードとして扱われます。 複...fess.codelibs.org/ja/14.13/user/search-wildcard.html -
データストアクロール
たとえば、/home/taro/csv ディレクトリに test.csv ファイルを以下のような内容で生成しておきます。...たとえば、/home/taro/csv ディレクトリに test.csv ファイルを以下のような内容で生成しておきます。...fess.codelibs.org/ja/14.13/admin/dataconfig-guide.html -
Installation
production environment or for load testing is not recommended with the...fess.codelibs.org/14.12/install/install.html -
Installation
production environment or for load testing is not recommended with the...fess.codelibs.org/14.13/install/install.html -
fess lastadoc
dfmail Test Mail TestmailPostcard 1 crawler.dfmail...String testmail.dfmail Title: Test Mail Class: org.codelibs.fe...fess.codelibs.org/lastadoc-fess.html -
sklearn.model_selection.TimeSeriesSplit — sciki...
in train/test sets. In each split, test indices must be...n_splits=5, test_size=None) >>> for i , ( train_index , test_index...scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html -
sklearn.model_selection.ShuffleSplit — scikit-l...
print ( f " Test: index= { test_index } " ) Fold 0: Train:...4] Test: index=[5 2] Fold 1: Train: index=[4 0 2 5] Test: index=[1...scikit-learn.org/stable/modules/generated/sklearn.model_selection.ShuffleSplit.html -
sklearn.model_selection.StratifiedShuffleSplit ...
Provides train/test indices to split data in train/test sets. This..." ) ... print ( f " Test: index= { test_index } " ) Fold 0: Train:...scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html -
Model selection: choosing estimators and their ...
X_digits [ test ], y_digits [ test ]) ... for train , test in k_fold..., y_train ) . score ( X_test , y_test )) >>> print ( scores )...scikit-learn.org/stable/tutorial/statistical_inference/model_selection.html