Explaining classifications for individual instances
Explaining classifications for individual instances
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DOI：
10.1109/tkde.2007.190734
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发表时间：
2008-05-01
影响因子：
8.9
通讯作者：
Kononenko, Igor
中科院分区：
文献类型：
--
作者：
Robnik-Sikonja, Marko;Kononenko, Igor
我们提出了一种解释单个实例的预测的方法。所提出的方法是通用的，可以与输出概率的所有分类模型一起使用。它基于模型对每个属性的单独贡献的预测的分解。我们的方法适用于所谓的黑盒模型，例如支持向量机、神经网络和最近邻算法，以及集成方法，例如提升和随机森林。我们证明生成的解释紧密遵循学习的模型，并提出了一种可视化技术，该技术显示了我们的方法的实用性并能够比较不同的预测方法。
We present a method for explaining predictions for individual instances. The presented approach is general and can be used with all classification models that output probabilities. It is based on the decomposition of a model's predictions on individual contributions of each attribute. Our method works for the so-called black box models such as support vector machines, neural networks, and nearest neighbor algorithms, as well as for ensemble methods such as boosting and random forests. We demonstrate that the generated explanations closely follow the learned models and present a visualization technique that shows the utility of our approach and enables the comparison of different prediction methods.