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“I rest and I rust”
-MARTIN LUTHER
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Latest from the Blog
Shall we worry about current AI taking over the world?
The recent spotlight on large language models (LLMs, such as Chatgpt) seems to have divided the public into two parties, debating whether they will take over human governance and control the world in the coming era of artificial intelligence, or whether they simply are tools to boost our productivity. What we should expect and what we could do in the right way to create reliable, trustworthy and safe AI?
![[Python Package] Permutation Feature-based Frequency Response Analysis](https://mjqtech.blog/wp-content/uploads/2023/06/pf-fra-5.png?w=800&h=600&crop=1)
[Python Package] Permutation Feature-based Frequency Response Analysis
PFFRA is an Interpretable Machine Learning technique to analyse the contribution of features in the frequency domain. This method is inspired by permutation feature importance analysis but aims to quantify and analyse the time-series predictive model’s mechanism from a global perspective.
反向回归中OLS估计的系统性偏差
回归模型通常并不研究变量间的因果关系，而是仅估计特征和目标变量之间的相关关系。然而，当我们试图沿着数据生成过程的反方向进行回归分析时会引入一个系统性的估计偏差。这种偏差形式上类似于衰减偏差，即回归系数被系统性低估，但其成因是回归方向与因果结构不一致，而非测量误差。本文旨在用简单数学推导证明有偏估计量总是在逆数据生成过程的回归分析中出现，并对此展示了一种可以调整该偏差量的方法。
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J. Mao's Tech House 