विधियों की तुलना करें

	एल्टमैन जेड-स्कोर: कॉर्पोरेट दिवालियापन की भविष्यवाणी	लॉजिस्टिक रिग्रेशन	XGBoost
क्षेत्र	वित्त	अनुसंधान सांख्यिकी	मशीन अधिगम
परिवार	Regression model	Process / pipeline	Machine learning
उद्भव वर्ष	1968	1958	2016
प्रवर्तक	Edward Altman	David Roxbee Cox	Chen, T. & Guestrin, C.
प्रकार	Multiple discriminant analysis scoring model	Method	Ensemble (gradient-boosted decision trees)
मौलिक स्रोत	Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
उपनाम	Altman's Z-Score Model, Multiple Discriminant Analysis Bankruptcy Model, Z-Score Financial Distress Model, Altman Z-Skoru	logit model, binomial logistic regression, LR	XGBoost, extreme gradient boosting, scalable tree boosting
संबंधित	3	3	5
सारांश	The Altman Z-Score is a linear discriminant model developed by Edward I. Altman in 1968 to predict corporate bankruptcy using five accounting-based financial ratios. Derived through multiple discriminant analysis on a matched sample of 66 US manufacturing firms, the model combines liquidity, profitability, leverage, solvency, and activity ratios into a single composite score that classifies firms as financially sound, distressed, or in a grey zone.	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.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.