线性回归模型 boston_linear.py
# coding=utf-8
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score,mean_squared_error,mean_absolute_error
from sklearn import preprocessing
# 加载波士顿房价数据
bt_X,bt_y = datasets.load_boston(return_X_y=True)
# 数据预处理：按列归一化
bt_X = preprocessing.scale(bt_X)
# 切分数据集：测试集 30%
bt_X_train,bt_X_test,bt_y_train,bt_y_test = train_test_split(bt_X,bt_y,test_size=0.3,random_state=0)
# 线性回归模型
from sklearn import linear_model
model = linear_model.LinearRegression()
# 模型训练
model.fit(bt_X_train,bt_y_train)
# 模型预测
bt_y_pred = model.predict(bt_X_test)
# 模型评估
# 均方误差
print("Mean squared error: %.3f" % mean_squared_error(bt_y_test, bt_y_pred))
# R方
print("Coefficient of determination: %.3f" % r2_score(bt_y_test, bt_y_pred))
