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今天读到一篇不错的文章,讨论了 IBM FreaAI 的一些分析过程,前面还有两篇概要也可以一起读。文章着重对树模型划分出来的各个小区间做评估,找到隐患最大的地方。大体的流程上是对原始特征做直方图,随后到 PDF,再不断便利 Y 轴找到既定的 metric 剧烈变化的地方。可以配合树模型自己算出来的信息熵一起用。这个技巧不是太常见,我只在两年前某一次 Kaggle 竞赛中遇到,举办方弄了一些诡异的合成数据…… https://towardsdatascience.com/how-to-find-weaknesses-in-your-machine-learning-models-3bcce3c7d71e



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今天读到一篇不错的文章,讨论了 IBM FreaAI 的一些分析过程,前面还有两篇概要也可以一起读。文章着重对树模型划分出来的各个小区间做评估,找到隐患最大的地方。大体的流程上是对原始特征做直方图,随后到 PDF,再不断便利 Y 轴找到既定的 metric 剧烈变化的地方。可以配合树模型自己算出来的信息熵一起用。这个技巧不是太常见,我只在两年前某一次 Kaggle 竞赛中遇到,举办方弄了一些诡异的合成数据…… https://towardsdatascience.com/how-to-find-weaknesses-in-your-machine-learning-models-3bcce3c7d71e

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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.

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