পদ্ধতির তুলনা করুন

	মাল্টি-লেয়ার পারসেপ্ট্রন (MLP)	লজিস্টিক রিগ্রেশন
ক্ষেত্র	যন্ত্র শিখন	গবেষণা পরিসংখ্যান
পরিবার	Machine learning	Process / pipeline
উদ্ভবের বছর	1986	1958
প্রবর্তক	Rumelhart, D. E., Hinton, G. E., & Williams, R. J.	David Roxbee Cox
ধরন	Feedforward neural network (supervised learning)	Method
মৌলিক উৎস	Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
অপর নাম	MLP, feedforward neural network, fully connected neural network, artificial neural network	logit model, binomial logistic regression, LR
সম্পর্কিত	4	3
সারসংক্ষেপ	The Multi-layer Perceptron (MLP) is a feedforward neural network architecture trained by backpropagation, formalised by Rumelhart, Hinton, and Williams in their landmark 1986 Nature paper. Composed of an input layer, one or more hidden layers of neurons with nonlinear activation functions, and an output layer, the MLP can approximate any continuous function to arbitrary accuracy and serves as the conceptual bridge between classical machine learning and modern deep learning.	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.