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from sklearn import datasets
from sklearn.linear_model import LogisticRegression
iris = datasets.load_iris()
X = iris['data']
y = iris['target']
logit = LogisticRegression(max_iter = 10000)
print(logit.fit(X,y))
print(logit.score(X,y))
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=None, max_iter=10000,
                   multi_class='auto', n_jobs=None, penalty='l2',
                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,
                   warm_start=False)
0.9733333333333334