原标题:训练模型的保存与加载
原文来自:CSDN 原文链接:https://blog.csdn.net/qq_36853469/article/details/103578466
1.目的:
将训练好的模型保存下来,已备下次使用,节省训练时间,提高效率
2.API:
from sklearn.externals import joblib
保存:
joblib.dump(rf,"test.pkl")
加载:
estimator = joblib.load("test.pkl")
3.Python代码实现:
# -*- coding: UTF-8 -*-
'''
@Author :Jason
波士顿房价预测,将模型保存到
'''
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error
from sklearn.externals import joblib
def model_save_fetch():
"""
岭回归对波士顿房价进行预测
:return:
"""
# 1)获取数据
boston = load_boston()
print("特征数量:n", boston.data.shape)
# 2)划分数据集
x_train, x_test, y_train, y_test = train_test_split(boston.data, boston.target, random_state=22)
# 3)标准化
transfer = StandardScaler()
x_train = transfer.fit_transform(x_train)
x_test = transfer.transform(x_test)
# # 4)预估器
# estimator = Ridge(alpha=0.5, max_iter=10000)
# estimator.fit(x_train, y_train)
#
# # 保存模型
# joblib.dump(estimator, "./files/test.pkl")
# 加载模型
estimator = joblib.load("./files/test.pkl")
# 5)得出模型
print("岭回归-权重系数为:n", estimator.coef_)
print("岭回归-偏置为:n", estimator.intercept_)
# 6)模型评估
y_predict = estimator.predict(x_test)
print("预测房价:n", y_predict)
error = mean_squared_error(y_test, y_predict)
print("岭回归-均方误差为:n", error)
return None
if __name__ == "__main__":
model_save_fetch()
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