পদ্ধতির তুলনা করুন

	অ্যাডাবুস্ট	সিদ্ধান্ত বৃক্ষ	লজিস্টিক রিগ্রেশন
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন	গবেষণা পরিসংখ্যান
পরিবার	Machine learning	Machine learning	Process / pipeline
উদ্ভবের বছর	1997	1984	1958
প্রবর্তক	Freund, Y. & Schapire, R.E.	Breiman, Friedman, Olshen & Stone	David Roxbee Cox
ধরন	Ensemble (sequential boosting of weak learners)	Recursive partitioning (if-then rules)	Method
মৌলিক উৎস	Freund, Y. & Schapire, R.E. (1997). A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55(1), 119–139.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
অপর নাম	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	logit model, binomial logistic regression, LR
সম্পর্কিত	5	5	3
সারসংক্ষেপ	AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.	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.