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Effector: A Python-based Machine Learning Library Dedicated to Regional Feature Effects
Global feature effects methods like Partial Dependence Plots (PDP) and SHAP Dependence Plots are commonly used to explain black-box models. However, they fall short when the model exhibits interactions between features or when local effects are heterogeneous. This can lead to misleading interpretations. Effector aims to address these limitations by providing regional feature effect methods, especially in crucial domains like healthcare and finance.
Key Features
Effector partitions the input space into subspaces to provide a regional explanation within each, reducing aggregation bias and increasing the interpretability and trustworthiness of machine learning models. It offers a comprehensive range of global and regional effect methods, including PDP, derivative-PDP, Accumulated Local Effects (ALE), Robust and Heterogeneity-aware ALE (RHALE), and SHAP Dependence Plots. The library’s modular design allows easy integration of new methods and ensures adaptability to emerging research in the field of XAI.
Practical Applications
Effector’s performance has been evaluated using both synthetic and real datasets, revealing insights into patterns that were not apparent with global effect methods alone. Its accessibility and ease of use make it a valuable tool for researchers and practitioners in the field of machine learning. Effector’s extensible design encourages collaboration and innovation, allowing researchers to experiment with novel methods and compare them with existing approaches.
Value Proposition
Effector offers a promising solution to the challenges of explainability in machine learning models. It makes black-box models easier to understand and more reliable by providing regional explanations that take into account heterogeneity and feature interactions. This ultimately speeds up the development and use of AI systems in real-world situations.
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