Interpretable Machine Learning

The goal of Interpretable Machine Learning is to allow oversight and understanding of machine-learned decisions. Much of the work in Interpretable Machine Learning has come in the form of devising methods to better explain the predictions of machine learning models. Source: [Assessing the Local Interpretability of Machine Learning Models ](https://arxiv.org/abs/1902.03501)

相关学科： SHAPFeature ImportanceInterpretabilityExplainable Artificial IntelligenceLIMECounterfactual ExplanationCounterfactual ReasoningKnowledge Graph EmbeddingsData PoisoningAdditive models

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