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Facilitating extraction of individual customer level rationales utilizing deep learning neural networks coupled with interpretability-oriented feature engineering and post-processing
Facilitating extraction of individual customer level rationales utilizing deep learning neural networks coupled with interpretability-oriented feature engineering and post-processing
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机译:利用深度学习神经网络,结合面向可解释性的特征工程和后处理,促进提取单个客户层面的基本原理
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摘要
The disclosure relates to extraction of rationales for studied outcome. A method comprises: grouping features as expert to align with a set of operating practices; generating interpretable features using operating rules, combining with statistical dependence analysis to bin selected features to generate favorite practice actions; grouping features as expert that combine a subset of the interpretable features to align with a set of operating practices. The method can also comprise: using a neural network or deep learning component to quantify contribution of respective experts at a consumer level applying a generic additive approach; extracting feature importance at an individual consumer-level decomposed from expert level importance; evaluating alternative, what-if, scenarios through sensitivity analysis to identify favorite practice actions; consolidating a subset of the practice actions at client or stakeholder levels; and routing respective practice actions as a function of responsibility for the set of operating practices to stakeholders or consumers.
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