Pages that link to "Item:Q2060438"
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The following pages link to Machine learning for credit scoring: improving logistic regression with non-linear decision-tree effects (Q2060438):
Displaying 16 items.
- Development and application of consumer credit scoring models using profit-based classification measures (Q144224) (← links)
- Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research (Q319944) (← links)
- Credit default prediction from user-generated text in peer-to-peer lending using deep learning (Q2140350) (← links)
- Deep learning for credit scoring: do or don't? (Q2239871) (← links)
- Mining the customer credit using classification and regression tree and multivariate adaptive regression splines (Q2257606) (← links)
- Advances in credit scoring: combining performance and interpretation in kernel discriminant analysis (Q2418293) (← links)
- EFFECT OF THE COMPANY RELATIONSHIP NETWORK ON DEFAULT PREDICTION: EVIDENCE FROM CHINESE LISTED COMPANIES (Q5048583) (← links)
- Bayesian credit ratings: A random forest alternative approach (Q5368771) (← links)
- Diagrammatic Representation and Inference (Q5714091) (← links)
- Interpretable machine learning for imbalanced credit scoring datasets (Q6069240) (← links)
- Machine learning in bank merger prediction: a text-based approach (Q6090183) (← links)
- The profitability of online loans: a competing risks analysis on default and prepayment (Q6106515) (← links)
- Improved credit risk prediction based on an integrated graph representation learning approach with graph transformation (Q6554678) (← links)
- Supervised feature compression based on counterfactual analysis (Q6572856) (← links)
- Interpretable generalized additive neural networks (Q6572862) (← links)
- An explainable federated learning and blockchain-based secure credit modeling method (Q6572887) (← links)
