sklearn中模型的保存与加载的api：sklearn.externals.joblib
对【学习笔记】回归算法-线性回归中的波士顿房价的模型进行保存：
from sklearn.externals import joblib
...
# 正则方程求解预测结果
lr = LinearRegression()
lr.fit(x_train, y_train)
# 保存训练好的模型
joblib.dump(lr, "./lr_model.pkl")
上例中保存的文件的扩展名为：pkl
加载上面保存的模型：
# 预测房价结果
model = joblib.load("./lr_model.pkl")
y_predict = std_y.inverse_transform(model.predict(x_test))
print("保存的模型的预测结果：", y_predict)
输出结果：
保存的模型的预测结果： [[17.37118212]
[34.56709952]
[17.4305089 ]
[23.35163525]
[16.75507239]
[38.7172448 ]
[21.60892137]
[35.84302277]
[29.98418551]
[13.74507248]
[20.41994648]
[33.9901789 ]
[25.11577134]
[ 8.53038073]
[20.60776675]
[21.90426029]
[13.45733183]
[22.46376949]
[20.39371985]
[18.76864034]
[11.38671154]
[20.05953434]
[12.83015496]
[12.03010661]
[18.23773943]
[31.06620129]
[ 5.56241134]
[12.98516251]
[10.91820687]
[13.11476316]
[ 3.78231428]
[28.73669394]
[10.77064138]
[17.84583808]
[25.70115301]
[18.45386837]
[30.85911707]
[19.05063058]
[26.20586891]
[12.48191789]
[13.47998438]
[14.06211429]
[19.62317357]
[19.44512303]
[27.88735019]
[15.32864261]
[22.35533616]
[30.43356824]
[39.3659543 ]
[28.09146432]
[12.90029862]
[15.80092028]
[41.12776075]
[35.45080887]
[18.28501264]
[24.91455836]
[20.79142213]
[36.23018652]
[28.69445038]
[15.13743074]
[11.12377075]
[ 7.1662545 ]
[18.92895135]
[25.14331425]
[22.24401089]
[ 9.26097072]
[19.36257124]
[ 5.46575337]
[26.35382739]
[19.46779945]
[17.61702798]
[20.11687972]
[21.58956195]
[25.27759462]
[13.37850839]
[25.97373011]
[12.4729385 ]
[24.9088518 ]
[19.71461561]
[12.47105092]
[22.26188696]
[29.08661824]
[14.97712477]
[40.28048188]
[12.564701 ]
[15.18255318]
[41.16108541]
[22.25338689]
[28.38662329]
[28.86476611]
[29.88912828]
[ 0.90732544]
[27.64437037]
[18.17414487]
[15.46396621]
[19.57395703]
[43.21673774]
[38.70313648]
[19.01216829]
[ 8.95379812]
[16.32508425]
[13.84733386]
[19.38368994]
[17.64480329]
[16.73515891]
[28.46209791]
[25.58264861]
[27.29229673]
[18.42422801]
[22.47274896]
[22.60092951]
[14.65879169]
[25.35595994]
[ 8.36124205]
[31.96224201]
[22.12208782]
[22.64038758]
[21.70722241]
[21.08181869]
[14.5844319 ]
[20.27973381]
[22.58921349]
[31.44559491]
[35.07616818]
[21.12770672]
[37.09917083]
[16.49457446]]