• Corpus ID: 237416659

Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process

@article{Molnar2021RelatingTP,
  title={Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process},
  author={Christoph Molnar and Timo Freiesleben and Gunnar Konig and Giuseppe Casalicchio and Marvin N. Wright and B. Bischl},
  journal={ArXiv},
  year={2021},
  volume={abs/2109.01433}
}
Scientists and practitioners increasingly rely on machine learning to model data and draw conclusions. Compared to statistical modeling approaches, machine learning makes fewer explicit assumptions about data structures, such as linearity. However, their model parameters usually cannot be easily related to the data generating process. To learn about the modeled relationships, partial dependence (PD) plots and permutation feature importance (PFI) are often used as interpretation methods. However… 

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