Type I error:“false positive” the error of rejecting a null hypothesis when it is actually true. (没有significant, 但它说有) Type II error: “false negative” the error of not rejecting a null hypothesis
均方误差(Mean Squared Error, MSE)与交叉熵(Cross-Entropy)的数学原理 1. 均方误差(Mean Squared Error, MSE) 均方误差主要用于回归问题,度量预测值与实际值之间的平均平方差。其数学公式为: MSE = 1 n ∑ i = 1 n ( y i − y ^ i ) 2 \text{MSE} = \frac{1}{n} \sum_{i
文章目录 nn.MSELoss() 均方误差损失函数参数数学公式元素版本 要点附录 参考链接 nn.MSELoss() 均方误差损失函数 torch.nn.MSELoss(size_average=None, reduce=None, reduction='mean') Creates a criterion that measures the mean squared err