Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Kredīta novērtēšana (rezultātu kartes, WoE/IV)× | Logistiskā regresija× | |
|---|---|---|
| Nozare≠ | Finanses | Pētniecības statistika |
| Saime≠ | Regression model | Process / pipeline |
| Izcelsmes gads≠ | 1997 | 1958 |
| Autors≠ | Hand & Henley; Thomas, Edelman & Crook | David Roxbee Cox |
| Tips≠ | Supervised binary classification model | Method |
| Pirmavots≠ | Hand, D. J., & Henley, W. E. (1997). Statistical classification methods in consumer credit scoring: a review. Journal of the Royal Statistical Society: Series A, 160(3), 523–541. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Citi nosaukumi≠ | Credit Scorecard, Application Scoring, Behavioural Scoring, Kredi Skorlama | logit model, binomial logistic regression, LR |
| Saistītās | 3 | 3 |
| Kopsavilkums≠ | Credit scoring is a statistical technique that estimates the probability that a borrower will default on a financial obligation. Using Weight of Evidence (WoE) binning, Information Value (IV) variable selection, and logistic regression, it converts raw applicant data into a single integer score. Formalized by Hand and Henley (1997) and elaborated by Thomas, Edelman, and Crook, the scorecard framework has become the regulatory standard for retail credit risk assessment in banking, lending, and insurance. | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. |
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