International scientific-practical conference on the theme:
«INFORMATION TECHNOLOGY, NETWORKS AND TELECOMMUNICATIONS
ITN&T-2023»
546 |
B.I. Sabirov, S.A. Raximberdiyev.
LOGISTIK REGRESSIYA MODELI
LOGISTIK REGRESSIYA MODELI
Sabirov Bahrombek Ilhombekovich
assistent o`qituvchi TATU Urganch
filiali
bahrombek0960@mail.ru
Raximberdiyev Sanjabek Alisher o’g’li
Axborot xavfsizligi 4-bosqich talabasi
TATU Urganch filiali
Sanjabek14@mail.ru
Annotation: Logistic regression is another technique borrowed from the field of statistics
through machine learning. These binary classification problems
are the basic way to solve
problems with two class values. In this post, you will learn how to use a machine learning logistic
regression algorithm to determine expected and expected outcomes and actions.
Regression analysis is widely used for forecasting and forecasting, where its use overlaps
significantly with the field of machine learning.
In some cases, regression analysis can be used to
determine causal relationships between independent and dependent variables. Importantly,
regressions by themselves only reveal the relationship between a dependent variable and a set of
independent variables in a fixed set of data. To use regressions to predict or infer causal
relationships, the researcher must carefully justify why an existing
relationship has predictive
power for a new context or why a relationship between two variables has a causal interpretation.
Keywords: Regression modeling, Logistic regression,
machine learning, EXP ()
expandenta function, Machine learning, Learning based on artificial intelligence approach.
Аннотация: Logistik regressiya - bu mashinani o'rganish orqali statistika sohasidan
olingan yana bir usul. Bu ikkilik tasniflash masalalari ikki sinf qiymatlari bilan muammolarni hal
qilishning asosiy usuli hisoblanadi. Ushbu postda siz kutilgan va kutilgan natijalar va harakatlarni
aniqlash uchun mashinani o'rganish logistik regressiya algoritmidan
qanday foydalanishni
o'rganasiz.
Regressiya tahlili bashorat qilish va bashorat qilish uchun keng qo'llaniladi, bu erda undan
foydalanish mashinani o'rganish sohasi bilan sezilarli darajada mos keladi. Ba'zi hollarda
regressiya tahlili mustaqil va qaram o'zgaruvchilar o'rtasidagi sabab-oqibat munosabatlarini
aniqlash uchun ishlatilishi mumkin.
Muhimi shundaki, regressiyalar o'z-o'zidan faqat qat'iy
ma'lumotlar to'plamidagi qaram o'zgaruvchi va mustaqil o'zgaruvchilar to'plami o'rtasidagi
munosabatlarni ochib beradi. Regressiyalarni bashorat qilish yoki sabab-oqibat munosabatlarini
xulosa qilish uchun ishlatish uchun tadqiqotchi nima uchun mavjud munosabatlar yangi kontekst
uchun bashorat qilish kuchiga ega ekanligini yoki nima uchun ikkita o'zgaruvchi o'rtasidagi
munosabat sabab-oqibat talqiniga ega ekanligini diqqat bilan asoslashi kerak.
Kalit so'zlar: Regression modellashtirish, logistik regressiya, mashinani o'rganish, EXP()
kengaytmasi funktsiyasi, mashinani o'rganish, sun'iy intellekt yondashuviga asoslangan o'rganish.
Regressiya. Statistik modellashtirishda regressiya tahlili - bu bog’liq o'zgaruvchi
va bir
yoki bir nechta mustaqil o'zgaruvchilar o'rtasidagi munosabatlarni baholash uchun statistik
jarayonlar to'plami. "tushuntiruvchi o'zgaruvchilar" yoki "xususiyatlar". Regressiya tahlilining eng
