python-scikit-learnLinear regression summary
import numpy as np
from sklearn import datasets, linear_model
X, y = datasets.load_diabetes(return_X_y=True)
model = linear_model.LinearRegression()
model.fit(X, y)
intercept = model.intercept_
coefs = model.coef_
score = model.score(X, y)ctrl + cfrom sklearn importimport module from scikit-learn | import numpyimport Numpy module |
datasets.load_diabetesloads sample diabetes database | linear_model.LinearRegressioninitialize linear regression model |
.fit(train model with a given features and target variable dataset | interceptvalue of linear model intercept after training |
coefslist of trained coefficients | scoretrained model |
Usage example
import numpy as np
from sklearn import datasets, linear_model
X, y = datasets.load_diabetes(return_X_y=True)
model = linear_model.LinearRegression()
model.fit(X, y)
intercept = model.intercept_
coefs = model.coef_
score = model.score(X, y)
print('Intercept:', intercept)
print('Coefs:', coefs)
print('R2:', score)output
Intercept: 152.13348416289597
Coefs: [ -10.0098663 -239.81564367 519.84592005 324.3846455 -792.17563855
476.73902101 101.04326794 177.06323767 751.27369956 67.62669218]
R2: 0.5177484222203498
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