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# data by the first feature. In practice, one would use a splitting according
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# to the use case at hand.
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+ selected_indices = (X_train [:, 0 ] < np .mean (X_train [:, 0 ])).astype (int )
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resampling_strategy = sklearn .model_selection .PredefinedSplit (
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- test_fold = np . where ( X_train [:, 0 ] < np . mean ( X_train [:, 0 ]))[ 0 ]
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+ test_fold = selected_indices
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)
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automl = autosklearn .classification .AutoSklearnClassifier (
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)
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automl .fit (X_train , y_train , dataset_name = 'breast_cancer' )
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+ print (automl .sprint_statistics ())
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+
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############################################################################
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# For custom resampling strategies (i.e. resampling strategies that are not
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# defined as strings by Auto-sklearn) it is necessary to perform a refit:
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