from sklearn.linear_model import LogisticRegression
clf = LogisticRegression(C=0.1, solver='liblinear')
# only prints parameters that have been changed from their default values
clf
LogisticRegression(C=0.1, solver='liblinear')
# see all parameters
clf.get_params()
{'C': 0.1, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'liblinear', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}
# restore the previous behavior
from sklearn import set_config
set_config(print_changed_only=False)
clf
LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True, intercept_scaling=1, l1_ratio=None, max_iter=100, multi_class='auto', n_jobs=None, penalty='l2', random_state=None, solver='liblinear', tol=0.0001, verbose=0, warm_start=False)
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