说到特征降维/选择的问题，大部分EDA的套路都是从model训练的loss来判断feature importance。其实有一个简单易行而且很有效的办法是在CV里面用做feature permutation，对原始特征shuffle得到shadow（也可以加一些噪音），在通过zscore比较两者差异来判断importance，不断遍历筛选。在ESLII中593页有提到这个办法。R里面有一个包Boruta可以做这件事，py也有：https://github.com/scikit-learn-contrib/boruta_py
BY Data Science Archive
Tata Power whose core business is to generate, transmit and distribute electricity has made no money to investors in the last one decade. That is a big blunder considering it is one of the largest power generation companies in the country. One of the reasons is the company's huge debt levels which stood at ₹43,559 crore at the end of March 2021 compared to the company’s market capitalisation of ₹44,447 crore.
The S&P 500 slumped 1.8% on Monday and Tuesday, thanks to China Evergrande, the Chinese property company that looks like it is ready to default on its more-than $300 billion in debt. Cries of the next Lehman Brothers—or maybe the next Silverado?—echoed through the canyons of Wall Street as investors prepared for the worst.
Data Science Archive from sg