python-scikit-learnHow to get logistic regression coefficients

from sklearn import datasets, linear_model, model_selection

X, y = datasets.load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)

model = linear_model.LogisticRegression()
model.fit(X_train, y_train)

coefs = model.coef_
intercept = model.intercept_ctrl + cgithub

`from sklearn import` import module from scikit-learn	`load_iris` loads Iris dataset
`model_selection.train_test_split` splits given `X` and `y` datasets to test (25% of values by default) and train (75% of values by default) subsets	`.LogisticRegression()` creates logistics regression model
`.fit(` train model with a given features and target variable dataset	`.coef_` returns list of coefficients of a trained model
`.intercept_` returns intercept of a trained model

Usage example

from sklearn import datasets, linear_model, model_selection

X, y = datasets.load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)

model = linear_model.LogisticRegression()
model.fit(X_train, y_train)

print(model.coef_)
print(model.intercept_)

output

[[-0.4618142   0.77963275 -2.30767749 -1.00587061]
 [ 0.53252788 -0.46624121 -0.09216546 -0.90155818]
 [-0.07071368 -0.31339155  2.39984295  1.90742879]]
[  9.64409977   2.01960555 -11.66370532]

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python-scikit-learnHow to get logistic regression coefficients

Usage example

output

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