Question

A biblioteca scikit-learn (versão 1.7.2) do Python (versão 3) oferece uma classe que implementa a regressão logística. Seleciona a alternativa que apresenta a sintaxe correta dessa classe.

Accepted Answer

E) class sklearn.linear_model.LogisticRegression(penalty='l2', *,
dual=False, tol=0.0001, C=1.0, fit_intercept=True,
intercept_scaling=1, class_weight=None,
random_state=None, solver='lbfgs', max_iter=100,
multi_class='deprecated', verbose=0, warm_start=False,
n_jobs=None, l1_ratio=None).

Answer

A) class sklearn.linear_model.MaxEnt(penalty='l2', *, dual=False,
tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1,
class_weight=None, random_state=None, solver='lbfgs',
max_iter=100, multi_class='deprecated', verbose=0,
warm_start=False, n_jobs=None, l1_ratio=None)

Answer

B) class sklearn.linear_model.Logit(penalty='l2', *, dual=False,
tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1,
class_weight=None, random_state=None, solver='lbfgs',
max_iter=100, multi_class='deprecated', verbose=0,
warm_start=True, l1_ratio=None).

Answer

C) class sklearn.linear_model.LogitRegression(penalty='l1', *,
dual=False, tol=0.1, C=1.0, fit_intercept=True,
intercept_scaling=10, class_weight=None, regression_step=
'1', random_state=None, solver='lbfgs', min_iter=1000,
warm_start=True, n_jobs=None, l1_ratio=None).

Answer

D) class sklearn.linear_model.Sigmoid(penalty='l1', *,
dual=False, tol=0.000001, C=1.0, fit_intercept=True,
intercept_scaling=10, class_weight=None,
random_state=None, solver='lbfgs', min_iter=1000,
multi_class='deprecated', verbose=1, warm_start=True,
n_jobs=None, l1_ratio=None).

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