Sklearn SVR模型实践 |
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线性回归模型: 流程: 读取数据,划分训练测试数据集,生成模型实例(SVR),预测,计算其loss值。 训练数据集拟合data与label之间的关系。 代码示例如下所示: 导入相应的库函数 # from sklearn.model_selection import train_test_split # from sklearn.preprocessing import StandardScaler from sklearn.svm import SVR from sklearn.metrics import mean_squared_error, mean_absolute_error import numpy as np import json读取数据、划分数据集等操作 train_data = json.load(open('./train_file_name')) X = np.asarray(train_data) #label data test_data = json.load(open('test_file_name')) y = np.asarray(test_data) def mape(y_ture,y_label): return np.mean(np.abs((y_true - y_label) / y_label)) x_train = x[:10000,:] x_test = x[10000:,:] y_train = y[:10000] y_test = y[10000:] linear_svr = SVR(kernel='linear') linear_svr.fit(x_train,y_train) linear_predict = linear_svr.predict(x_test) #calculate the different criterion mape(linear_predict,y_test) mean_squared_error(linear_predict,y_test) mean_absolute_error(linear_predict,y_test)
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