পদ্ধতির তুলনা করুন

	লজিস্টিক রিগ্রেশন	সাপোর্ট ভেক্টর মেশিন (শ্রেণীকরণ)	XGBoost
ক্ষেত্র	গবেষণা পরিসংখ্যান	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Process / pipeline	Machine learning	Machine learning
উদ্ভবের বছর	1958	1995	2016
প্রবর্তক	David Roxbee Cox	Cortes, C. & Vapnik, V.	Chen, T. & Guestrin, C.
ধরন	Method	Maximum-margin classifier (kernel method)	Ensemble (gradient-boosted decision trees)
মৌলিক উৎস	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
অপর নাম	logit model, binomial logistic regression, LR	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier	XGBoost, extreme gradient boosting, scalable tree boosting
সম্পর্কিত	3	5	5
সারসংক্ষেপ	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.	The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.	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.