线性回归的模型保存与加载

十一、模型的保存和加载

1.sklearn模型的保存和加载api

• from sklearn.externals import joblib
  • 保存：joblib.dump(estimator,‘test.pkl’)
  • 加载：estimator = joblib.load(‘test.pkl’)

2.线性回归的模型保存和加载案例

def load_dump_demo():
    '''
    模型保存和加载
    '''
    # 1.获取数据
    data = load_boston()
    # 2.数据集划分
    x_train, x_test, y_train, y_test = train_test_split(data.data, data.target, random_state=22)

    # 3.特征工程-标准化
    transfer = StandardScaler()
    x_train = transfer.fit_transform(x_train)
    x_test = transfer.fit_transform(x_test)
    
    # 4.机器学习-线性回归(岭回归)
    # # 4.1 模型训练
    # estimator = Ridge(alpha=1)
    # estimator.fit(x_train, y_train)
    #
    # # 4.2 模型保存
    # joblib.dump(estimator, "test.pkl")
    
     # 4.3 模型加载
    estimator = joblib.load("test.pkl")

    # 5.模型评估
    # 5.1 获取系数等值
    y_predict = estimator.predict(x_test)
    print("预测值为:\n", y_predict)
    print("模型中的系数为:\n", estimator.coef_)
    print("模型中的偏置为:\n", estimator.intercept_)
    
    # 5.2 评价
    # 均方误差
    error = mean_squared_error(y_test,y_predict)
    =print("误差为:",error)