As we rely more and more on machine learning models for real-life decision-making, being able to understand and trust the predictions becomes ever more important. Local explainer models have recently been introduced to explain the predictions of complex machine learning models at the instance level. In this paper, we propose Local Rule-based Model Interpretability with k-optimal Associations (LoRMIkA), a novel model-agnostic approach that obtains k-optimal association rules from a neighbourhood of the instance to be explained. Compared with other rule-based approaches in the literature, we argue that the most predictive rules are not necessarily the rules that provide the best explanations. Consequently, the LoRMIkA framework provides a flexible way to obtain predictive and interesting rules. It uses an efficient search algorithm guaranteed to find the k-optimal rules with respect to objectives such as confidence, lift, leverage, coverage, and support. It also provides multiple rules which explain the decision and counterfactual rules, which give indications for potential changes to obtain different outputs for given instances. We compare our approach to other state-of-the-art approaches in local model interpretability on three different datasets and achieve competitive results in terms of local accuracy and interpretability.

随着我们越来越依赖机器学习模型进行现实生活中的决策，能够理解和信任这些预测变得越来越重要。最近引入了本地解释器模型，以在实例级别解释复杂的机器学习模型的预测。在本文中，我们提出了一种基于局部规则的具有k最优关联的模型可解释性（LoRMIkA），这是一种与模型无关的新颖方法，可从要说明的实例的邻域获得k最优关联规则。与文献中其他基于规则的方法相比，我们认为最具预测性的规则不一定是提供最佳解释的规则。因此，LoRMIkA框架提供了一种获取预测性规则和有趣规则的灵活方法。它使用有效的搜索算法，保证找到与目标有关的k个最佳规则，例如置信度，提升，杠杆作用，覆盖率和支持度。它还提供了解释规则和反事实规则的多个规则，这些规则为潜在的变化提供了指示，以获得给定实例的不同输出。我们在三个不同的数据集上将我们的方法与其他最新方法的本地模型可解释性进行了比较，并在本地准确性和可解释性方面取得了竞争性的结果。