Local Explanation和Global Explanation中文解释
时间: 2024-01-17 21:03:52 浏览: 113
Local Explanation和Global Explanation是机器学习模型解释性的两个方面,其中文解释如下:
- Local Explanation(局部解释):解释一个特定样本或预测的输出结果,即解释模型在某个具体的输入上的决策过程和原因。局部解释通常是针对单个样本进行的,可以帮助我们理解模型在特定情况下的决策依据和重要特征。
- Global Explanation(全局解释):解释整个模型的行为和预测能力,即解释模型在整个数据集上的决策过程和原因。全局解释通常是通过统计分析和可视化方法来实现的,可以帮助我们理解模型的整体特征和重要特征,以及模型的优缺点。
总的来说,局部解释和全局解释都是解释机器学习模型的重要手段,可以帮助我们理解和信任模型的决策过程,从而更好地应用和优化这些模型。
相关问题
global explanation
全局性解释是指对某一现象、事件或问题进行深入分析、探讨和阐述,从全局视角出发,全面而系统地解释其背后的原因、机制和影响,以便更好地理解问题的本质和复杂性。全局性解释通常需要考虑多方面的影响因素、多种不同的角度和视角,并具有较高的综合性和深度。
mathematical explanation of diagonal loading
Diagonal loading is a technique used in signal processing to improve the performance of linear filters. It involves adding a small positive constant to the diagonal elements of the correlation matrix before computing its inverse.
In mathematical terms, suppose we have a correlation matrix R, which is a symmetric matrix of size N x N. The diagonal loading technique involves adding a positive constant, denoted by λ, to the diagonal elements of R, resulting in a modified correlation matrix Rλ:
Rλ = R + λ*I
where I is the identity matrix of size N x N.
The purpose of diagonal loading is to ensure that the inverse of Rλ exists and is well-conditioned, even if R is ill-conditioned. This is because adding λ*I to R effectively increases the diagonal elements of the matrix, which can help to stabilize the inverse computation and improve the numerical accuracy.
In practice, the value of λ is often chosen empirically, based on the specific application and the characteristics of the input data. Too small a value of λ may not have a noticeable effect on the filter performance, while too large a value may lead to over-regularization and reduced filter performance.
Overall, diagonal loading is a useful technique for improving the stability and performance of linear filters in various signal processing applications.
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