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multinomial regression scikit learn

Adventurous Alligator answered on August 25, 2020 Popularity 10/10 Helpfulness 5/10

answer multinomial regression scikit learn

multinomial regression scikit learn

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model1 = LogisticRegression(random_state=0, multi_class='multinomial', penalty='none', solver='newton-cg').fit(X_train, y_train)
preds = model1.predict(X_test)

#print the tunable parameters (They were not tuned in this example, everything kept as default)
params = model1.get_params()
print(params)

{'C': 1.0, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': None, 'penalty': 'none', 'random_state': 0, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}

Popularity 10/10 Helpfulness 5/10 Language python

Source: www.datasklr.com

Tags: multinomial python regression scikit-learn

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Contributed on Aug 25 2020

Adventurous Alligator

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multinomial regression scikit learn

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