## Surrogate Data Models: Interpreting Large-scale Machine Learning Crisis Prediction Models

_IMF Working Papers, February 24, 2023_

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## Bibliographic details
- Authors: Jorge A Chan-Lau, Ruofei Hu, Maksym Ivanyna, Ritong Qu, Cheng Zhong
- Published: February 24, 2023
- Series: IMF Working Papers
- DOI: https://doi.org/10.5089/9798400234828.001

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### Summary and purpose
- Machine learning models are becoming increasingly important in the prediction of economic crises.
- Large numbers of predictors (features) in these models impair model interpretability and their ability to provide adequate guidance in the design of crisis prevention and mitigation policies.
- This paper introduces surrogate data models as dimensionality reduction tools in large-scale crisis prediction models and assesses the appropriateness of this approach by applying surrogates to large-scale crisis prediction models developed at the IMF.
- The results are consistent with economic intuition and validate the use of surrogates as interpretability tools.

### Key findings
- Surrogate data models function as dimensionality reduction tools that improve interpretability of large-scale machine learning crisis prediction models.
- Application to IMF-developed large-scale crisis prediction models shows results that are consistent with economic intuition.
- Surrogates help bridge the gap between complex, high-dimensional predictive models and the actionable guidance needed for crisis prevention and mitigation policies.

### Subjects and keywords (as listed)
- Subjects: Deposit rates, Early warning systems, Financial crises, Financial services, Machine learning, Technology, Yield curve
- Keywords: Annex I. IMF ML, Crisis prediction, Deposit rates, Early warning systems, explainable models, Global, IMF ML crisis, machine learning, ML crisis prediction, model interpretability, surrogate data models, surrogates, Yield curve

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_Source: https://www.imf.org/en/publications/wp/issues/2023/02/24/surrogate-data-models-interpreting-large-scale-machine-learning-crisis-prediction-models-529723_