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Explaining classifications for individual instances
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DOI：10.1109/TKDE.2007.190734.png)
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摘要
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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.
Keyword:
machine learning
decision support
knowledge modeling
information visualization
model explanation
model comprehensibility
decision visualization
prediction models
classification
nearest neighbor
neural nets
support vector machines
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期刊
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10.4
论文数:
6.8K
被引数:
3.2W
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