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Machine Learning for Credit Scoring Insights by Mohamed Alhama is a document available to read on EtoBox.

What is Machine Learning for Credit Scoring Insights about?

- The document proposes a new hybrid credit scoring method called penalised logistic tree regression (PLTR) that combines machine learning and econometrics. - PLTR uses rules extracted from short-depth decision trees as predictors in a penalised logistic regression model to capture non-linear effects while maintaining the interpretability of logistic regression. - The goal is to improve the predictive power of logistic regression for credit scoring while avoiding the trade-off between performance and inte

Author

Mohamed Alhama

Language

EN