API
sklearn.externals.joblib
文件格式：pkl
代码示例
from sklearn.datasets import load_boston from sklearn.externals import joblib from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler # 加载数据 boston = load_boston() # 训练集，测试集拆分 X_train, X_test, y_train, y_test = train_test_split( boston.data, boston.target, test_size=0.25) # 数据标准化处理 # 特征值 标准化 std_x = StandardScaler() X_train = std_x.fit_transform(X_train) X_test = std_x.transform(X_test) # 目标值 标准化 std_y = StandardScaler() y_train = std_y.fit_transform(y_train.reshape(-1, 1)) y_test = std_y.transform(y_test.reshape(-1, 1)) # 训练数据并序列化训练结果 # lr = LinearRegression() # lr.fit(X_train, y_train) # joblib.dump(lr, "boston.pkl") # 反序列化保存的训练结果 lr = joblib.load("boston.pkl") y_lr_predict = std_y.inverse_transform(lr.predict(X_test)) print(y_lr_predict)
