[{"createTime":1735734952000,"id":1,"img":"hwy_ms_500_252.jpeg","link":"https://activity.huaweicloud.com/cps.html?fromacct=261f35b6-af54-4511-a2ca-910fa15905d1&utm_source=V1g3MDY4NTY=&utm_medium=cps&utm_campaign=201905","name":"华为云秒杀","status":9,"txt":"华为云38元秒杀","type":1,"updateTime":1735747411000,"userId":3},{"createTime":1736173885000,"id":2,"img":"txy_480_300.png","link":"https://cloud.tencent.com/act/cps/redirect?redirect=1077&cps_key=edb15096bfff75effaaa8c8bb66138bd&from=console","name":"腾讯云秒杀","status":9,"txt":"腾讯云限量秒杀","type":1,"updateTime":1736173885000,"userId":3},{"createTime":1736177492000,"id":3,"img":"aly_251_140.png","link":"https://www.aliyun.com/minisite/goods?userCode=pwp8kmv3","memo":"","name":"阿里云","status":9,"txt":"阿里云2折起","type":1,"updateTime":1736177492000,"userId":3},{"createTime":1735660800000,"id":4,"img":"vultr_560_300.png","link":"https://www.vultr.com/?ref=9603742-8H","name":"Vultr","status":9,"txt":"Vultr送$100","type":1,"updateTime":1735660800000,"userId":3},{"createTime":1735660800000,"id":5,"img":"jdy_663_320.jpg","link":"https://3.cn/2ay1-e5t","name":"京东云","status":9,"txt":"京东云特惠专区","type":1,"updateTime":1735660800000,"userId":3},{"createTime":1735660800000,"id":6,"img":"new_ads.png","link":"https://www.iodraw.com/ads","name":"发布广告","status":9,"txt":"发布广告","type":1,"updateTime":1735660800000,"userId":3},{"createTime":1735660800000,"id":7,"img":"yun_910_50.png","link":"https://activity.huaweicloud.com/discount_area_v5/index.html?fromacct=261f35b6-af54-4511-a2ca-910fa15905d1&utm_source=aXhpYW95YW5nOA===&utm_medium=cps&utm_campaign=201905","name":"底部","status":9,"txt":"高性能云服务器2折起","type":2,"updateTime":1735660800000,"userId":3}]
在Python中使用支持向量机（SVM）进行数据回归预测，你可以遵循以下步骤：
* 导入必要的库： from sklearn.svm import SVR from sklearn.model_selection import
train_test_splitfrom sklearn.metrics import mean_squared_error
*
准备数据集：
你需要准备你的特征矩阵X和目标变量向量y。确保X和y的维度匹配。
*
拆分数据集：
将数据集划分为训练集和测试集，一个常见的比例是将数据的70%用于训练，30%用于测试：
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,
random_state=0)
* 创建并拟合模型：
创建SVM回归模型，并使用训练集进行拟合： regressor = SVR(kernel='rbf') regressor.fit(X_train,
y_train)
这里的kernel参数指定了核函数的类型，rbf表示径向基核函数，你也可以根据需要选择其他核函数。
* 进行预测：
使用测试集数据进行预测： y_pred = regressor.predict(X_test)
* 评估模型：
通过计算均方误差（Mean Squared Error, MSE）或其他适当的指标来评估模型的性能： mse = mean_squared_error(
y_test, y_pred)
这样，你就可以使用支持向量机（SVM）模型进行数据回归预测了。记得根据实际问题对SVM的参数进行调优，例如调整核函数类型、正则化参数等。
