损失函数作用_损失函数

损失函数作用

Loss function describes how efficient the model performs with respect to the expected outcome. Here, the main objective is to minimize the number of misclassifications. The choice of the loss function is critical in defining the outputs in a way that is sensitive to the application at hand. There are various types of lost or cost functions.

损失函数描述了模型相对于预期结果的执行效率。 在此，主要目的是最大程度地减少错误分类的次数。 损耗函数的选择对于以对当前应用程序敏感的方式定义输出至关重要。 有各种类型的损失或成本函数。

L1损失 (L1 Loss)

L1 loss function is also known as least absolute deviations (LAD). It is the sum of all the absolute differences between the target value and the predicted values.

L1损失函数也称为最小绝对偏差(LAD)。 它是目标值和预测值之间所有绝对差的总和。

L2损失 (L2 Loss)

L2 loss function is also known as least squares error (LSE). It is basically minimizing the sum of square of the differences between the target value and the predicted values.

L2损失函数也称为最小二乘误差(LSE)。 基本上是将目标值和预测值之间的差的平方和最小化。