机器学习sklearn多元线性回归2

from __future__ import print_functionfrom sklearn import datasetsfrom sklearn.linear_model import LinearRegressionloaded_data = datasets.load_boston()data_X = loaded_data.datadata_y = loaded_data.targetmodel = LinearRegression()model.fit(data_X, data_y)print(model.predict(data_X[:4, :]))# model.coef_表示的是x项前的参数，比如y=0.1x+3，model.coef_表示的是0.1，print(model.coef_)# model.intercept_表示的是截距，比如y=0.1x+3，model.intercept_表示的是3，print(model.intercept_)# 得到之前定义的参数print(model.get_params())# 对预测进行打分，用data_X预测的值和data_y进行对比print(model.score(data_X, data_y))