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 + cgithub

`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

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python-scikit-learnR2 score for linear regression

Usage example

output

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