sgdr.predict
时间: 2023-10-31 13:36:09 浏览: 50
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sgdr.predict(x)
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sgdr = SGDRegressor(max_iter=1000) sgdr.fit(X_norm, y_train) print(sgdr) print(f"number of iterations completed: {sgdr.n_iter_}, number of weight updates: {sgdr.t_}")
这段代码使用了scikit-learn库中的SGDRegressor类来进行线性回归模型的训练。
首先,创建了一个SGDRegressor对象,命名为sgdr,并设置最大迭代次数为1000。
然后,使用fit方法将标准化后的数据集X_norm和目标变量y_train输入到模型中进行训练。
接下来,使用print函数输出sgdr对象的信息,这将显示模型的参数设置和其他相关信息。
最后,使用f-string格式化输出sgdr对象的迭代次数(n_iter_)和权重更新次数(t_)。
这段代码的目的是训练一个线性回归模型,并打印出相关的信息和迭代次数以及权重更新次数。
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