مقایسهٔ روش‌ها

	امتیاز Z آلتمن: پیش‌بینی ورشکستگی شرکت‌ها	رگرسیون لجستیک	XGBoost
حوزه	مالی	آمار پژوهش	یادگیری ماشین
خانواده	Regression model	Process / pipeline	Machine learning
سال پیدایش	1968	1958	2016
پدیدآور	Edward Altman	David Roxbee Cox	Chen, T. & Guestrin, C.
نوع	Multiple discriminant analysis scoring model	Method	Ensemble (gradient-boosted decision trees)
منبع بنیادین	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.
نام‌های دیگر	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
مرتبط	3	3	5
خلاصه	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.