python廣義線性模型是什么

import matplotlib.pyplot as pltimport numpy as npfrom sklearn import datasets, linear_modelfrom sklearn.metrics import mean_squared_error, r2_score
# Load the diabetes datasetdiabetes = datasets.load_diabetes()

# Use only one featurediabetes_X = diabetes.data[:, np.newaxis, 2]
# Split the data into training/testing setsdiabetes_X_train = diabetes_X[:-20]diabetes_X_test = diabetes_X[-20:]
# Split the targets into training/testing setsdiabetes_y_train = diabetes.target[:-20]diabetes_y_test = diabetes.target[-20:]
# Create linear regression objectregr = linear_model.LinearRegression()
# Train the model using the training setsregr.fit(diabetes_X_train, diabetes_y_train)
# Make predictions using the testing setdiabetes_y_pred = regr.predict(diabetes_X_test)
# The coefficientsprint('Coefficients: \n', regr.coef_)# The mean squared errorprint("Mean squared error: %.2f"      % mean_squared_error(diabetes_y_test, diabetes_y_pred))# Explained variance score: 1 is perfect predictionprint('Variance score: %.2f' % r2_score(diabetes_y_test, diabetes_y_pred))
# Plot outputsplt.scatter(diabetes_X_test, diabetes_y_test,  color='black')plt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3)
plt.xticks(())plt.yticks(())
plt.show()