from __future__ import print_function from sklearn import datasets from sklearn.linear_model import LinearRegression import matplotlib.pyplot as plt loaded_data = datasets.load_boston() data_X = loaded_data.data data_y = loaded_data.target model = LinearRegression() model.fit(data_X, data_y) # print(model.predict(data_X[:4, :])) # print(data_y[:4]) print(model.coef_) print(model.intercept_) print(model.get_params()) # X, y = datasets.make_regression(n_samples=100, n_features=1, n_targets=1, noise=5) # plt.scatter(X, y) # plt.show()
LinearRegression将方程分为两个部分存放，coef_存放回归系数，intercept_则存放截距, get_params 得到参数。
