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About Credit Risk Analysis Using Machine and Deep Learning
This document summarizes a research article that analyzes credit risk for enterprises using machine learning and deep learning models. It builds binary classifiers on real bank data to predict loan default probabilities. It finds that tree-based models like random forest and gradient boosting machines are more stable than neural network models in selecting important features and classifying loans. The document discusses the importance of comparing multiple models, data sets, and evaluation criteria to avoid bias and get a full picture of results before making decisions.
Detailed Information
| Author: | ['Matheus Silva'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 19 |
| Language: | English |
| Format: | |
| Price: | FREE |
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