Author:
Lars Fluri
Affiliation:
Department of Computational Economics and Finance, Department of Management Accounting University of Basel, Peter Merian-Weg 6, Basel, Switzerland
Keyword(s):
XAI, XML, SHAP, Shapley Value Sampling, DeepLIFT, LIME, Integrated Gradients, GradientSHAP, Deep Learning, Neural Networks, Finance.
Abstract:
This paper evaluates the effectiveness of different feature importance algorithms employed on a neural network, focused on target prediction tasks with varying data complexities. The study reveals that the feature importance algorithms excel with data featuring minimal correlation between the attributes. However, their determination considerably decreases with escalating levels of correlation, while the inclusion of irrelevant features has minimal impact on determination. In terms of predictive power, DeepLIFT surpasses other methods for most data cases, but falls short in total infidelity. For more complex cases, Shapley Value Sampling outperforms DeepLIFT. In an empirical application, Integrated Gradients and DeepLIFT demonstrate lower sensitivity and lower infidelity, respectively. this paper highlights interesting dynamics between predictive power and fidelity in feature importance algorithms and offers key insights for their application in complex data scenarios.