Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects
Summary
Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects is a scholarly article[1].
Key Facts
Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects's instance of is recorded as scholarly article[2].
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APA4ort.xyz Knowledge Graph. (2026). Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects. Retrieved May 24, 2026, from https://4ort.xyz/entity/machine-learning-for-credit-scoring-improving-logistic-regression-with-non-linear-decision-tree-effects
MLA“Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/machine-learning-for-credit-scoring-improving-logistic-regression-with-non-linear-decision-tree-effects.
BibTeX@misc{4ortxyz_machine-learning-for-credit-scoring-improving-logistic-regression-with-non-linear-decision-tree-effects_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects}}, year = {2026}, url = {https://4ort.xyz/entity/machine-learning-for-credit-scoring-improving-logistic-regression-with-non-linear-decision-tree-effects}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects — https://4ort.xyz/entity/machine-learning-for-credit-scoring-improving-logistic-regression-with-non-linear-decision-tree-effects (retrieved 2026-05-24)
