31 lines
1.0 KiB
Python
31 lines
1.0 KiB
Python
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"""
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=================================
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Compact estimator representations
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=================================
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This example illustrates the use of the print_changed_only global parameter.
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Setting print_changed_only to True will alterate the representation of
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estimators to only show the parameters that have been set to non-default
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values. This can be used to have more compact representations.
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"""
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print(__doc__)
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from sklearn.linear_model import LogisticRegression
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from sklearn import set_config
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lr = LogisticRegression(penalty='l1')
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print('Default representation:')
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print(lr)
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# LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
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# intercept_scaling=1, l1_ratio=None, max_iter=100,
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# multi_class='warn', n_jobs=None, penalty='l1',
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# random_state=None, solver='warn', tol=0.0001, verbose=0,
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# warm_start=False)
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set_config(print_changed_only=True)
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print('\nWith changed_only option:')
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print(lr)
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# LogisticRegression(penalty='l1')
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