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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



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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

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Telegram’s stand out feature is its encryption scheme that keeps messages and media secure in transit. The scheme is known as MTProto and is based on 256-bit AES encryption, RSA encryption, and Diffie-Hellman key exchange. The result of this complicated and technical-sounding jargon? A messaging service that claims to keep your data safe.Why do we say claims? When dealing with security, you always want to leave room for scrutiny, and a few cryptography experts have criticized the system. Overall, any level of encryption is better than none, but a level of discretion should always be observed with any online connected system, even Telegram.

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