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 + cgithub

`from sklearn import` import module from scikit-learn	`import numpy` import Numpy module
`datasets.load_diabetes` loads sample diabetes database	`linear_model.LinearRegression` initialize linear regression model
`.fit(` train model with a given features and target variable dataset	`intercept` value of linear model intercept after training
`coefs` list of trained coefficients	`score` trained model `R2` score

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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python-scikit-learnLinear regression summary

Usage example

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