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 + cfrom sklearn importimport module from scikit-learn | import numpyimport Numpy module |
datasets.load_diabetesloads sample diabetes database | model_selection.train_test_splitsplits given |
linear_model.LinearRegressioninitialize linear regression model | .fit(train model with a given features and target variable dataset |
scoretrained model | |
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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