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Performance Evaluation in Machine Learning: The Good, the Bad, the Ugly, and the Way Forward

机译:机器学习中的绩效评估:善,坏,丑,以及前进的方向

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This paper gives an overview of some ways in which our understanding of performance evaluation measures for machine-learned classifiers has improved over the last twenty years. I also highlight a range of areas where this understanding is still lacking, leading to ill-advised practices in classifier evaluation. This suggests that in order to make further progress we need to develop a proper measurement theory of machine learning. I then demonstrate by example what such a measurement theory might look like and what kinds of new results it would entail. Finally, I argue that key properties such as classification ability and data set difficulty are unlikely to be directly observable, suggesting the need for latent-variable models and causal inference.
机译:本文概述了我们对机器学习分类机的绩效评估措施的理解,在过去的二十年里有所改善。 我还突出了一系列理解仍然缺乏这种理解的领域,导致分类器评估中的不明意识。 这表明,为了进一步进步,我们需要开发机器学习的适当测量理论。 然后,我通过示例演示了这样的测量理论可能看起来像什么类型的新结果。 最后,我争辩说,诸如分类能力和数据集等的关键属性不太可能是直接可观察到的,这表明需要潜在变量模型和因果推断。

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