It takes the average reader and 31 minutes to read Surrogate Data Models: Interpreting Large-scale Machine Learning Crisis Prediction Models by Mr. Jorge A Chan-Lau
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Machine learning models are becoming increasingly important in the prediction of economic crises. The models, however, use datasets comprising a large number of predictors (features) which impairs 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. The appropriateness of this approach is assessed by their application 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.
Surrogate Data Models: Interpreting Large-scale Machine Learning Crisis Prediction Models by Mr. Jorge A Chan-Lau is 31 pages long, and a total of 7,781 words.
This makes it 10% the length of the average book. It also has 10% more words than the average book.
The average oral reading speed is 183 words per minute. This means it takes and 42 minutes to read Surrogate Data Models: Interpreting Large-scale Machine Learning Crisis Prediction Models aloud.
Surrogate Data Models: Interpreting Large-scale Machine Learning Crisis Prediction Models is suitable for students ages 8 and up.
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