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python-scikit-learnR2 score for linear regression


import numpy as np
from sklearn import datasets, linear_model, model_selection

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

model = linear_model.LinearRegression()
model.fit(X_train, y_train)
r2 = model.score(X_test, y_test)ctrl + c
from sklearn import

import module from scikit-learn

import numpy

import Numpy module

datasets.load_diabetes

loads sample diabetes database

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

linear_model.LinearRegression

initialize linear regression model

.fit(

train model with a given features and target variable dataset

score

trained model R2 score for a given (test) dataset


Usage example

import numpy as np
from sklearn import datasets, linear_model, model_selection, metrics

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

model = linear_model.LinearRegression()
model.fit(X_train, y_train)
r2 = model.score(X_test, y_test)

print(r2)
output
0.5278235386853034