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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 + c
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