下面通过线性回归器对美国波士顿地区的房价进行预测。

准备数据集

from sklearn.datasets import load_boston
boston = load_boston()
print(boston.DESCR)

分割数据集

from sklearn.cross_validation import train_test_split
import numpy as np
X = boston.data
y = boston.target
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33, test_size=0.25)

分析回归目标值

print("The max target value is", np.max(boston.target))
print("The min target value is", np.min(boston.target))
print("The average target value is", np.mean(boston.target))

对数据标准化处理

from sklearn.preprocessing import StandardScaler
ss_X = StandardScaler()
ss_y = StandardScaler()
X_train = ss_X.fit_transform(X_train)
X_test = ss_X.transform(X_test)

y_train = ss_y.fit_transform(y_train)
y_test = ss_y.transform(y_test)

训练线性回归预测模型

from sklearn.linear_model import LinearRegression
lr = LinearRegression()
lr.fit(X_train, y_train)
lr_y_predict = lr.predict(X_test)

通过SGDRegressor训练模型

from sklearn.linear_model import SGDRegressor
sgdr = SGDRegressor()
sgdr.fit(X_train, y_train)
sgdr_y_predict = sgdr.predict(X_test)

LinearRegression测试评估

print('The value of default measurement of LinearRegression is', lr.score(X_test, y_test))
from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error

print('The value of R-squared of LinearRegression is', r2_score(y_test, lr_y_predict))
print('The mean squared error of LinearRegression is', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(lr_y_predict)))
print('The mean absoluate error of LinearRegression is', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(lr_y_predict)))

SGDRegressor模型测试评估

print('The value of default measurement of SGDRegressor is', sgdr.score(X_test, y_test))
print('The value of R-squared of SGDRegressor is', r2_score(y_test, sgdr_y_predict))
print('The mean squared error of SGDRegressor is', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(sgdr_y_predict)))
print('The mean absoluate error of SGDRegressor is', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(sgdr_y_predict)))