Classification is a central discipline of machine learning (ML) and classifiers have become increasingly popular to support or replace human decisions. We encounter them as email spam detectors, as decision support systems, for example in healthcare, as aid in interpreting X-rays in breast cancer detection, or in the financial and insurance sector, for financial and risk analysis. For example, Facebook uses classifiers to predict the likelihood that users will navigate or click in a certain way, at scale, for millions and millions of users every day. They also play a significant role in various areas of computer vision, where traffic signals and other objects need to be identified in order to “read” a situation during assisted or autonomous driving.

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迈向机器学习的可解释性：形式化方法


分类是机器学习 (ML) 的核心学科，分类器在支持或取代人类决策方面变得越来越流行。我们将它们视为垃圾邮件检测器、决策支持系统，例如在医疗保健领域，帮助解释乳腺癌检测中的 X 射线，或者在金融和保险领域，用于财务和风险分析。例如，Facebook 使用分类器来预测用户每天以某种方式大规模导航或点击的可能性。它们还在计算机视觉的各个领域发挥着重要作用，其中需要识别交通信号和其他物体，以便“读取”辅助或自动驾驶期间的情况。