MAʼLUMOTLAR TOʻPLAMINI OʻQITISH, BAHOLASH VA TEST TOʻPLAMLARIGA AJRATISH USULLARI
Keywords:
maʼlumotlar toʻplamini oʻqitish, training set, o‘quv to‘plami, validation set, baholash to‘plami, test set, test to‘plami, Machine Learning, ML, samaradorlik, accuracy, aniqlik, precision, eslab qolish, recall, F1-ball, F1 scoreAbstract
Mashinali oʻqitish algoritmlari maʼlumotlardagi shablon(qolip)larni oʻrganadi va ulardan yangi maʼlumotlar boʻyicha bashorat qilish uchun foydalanadi. Ishlab chiqilgan sun`iy intellekt modellari ish faoliyatini ularning oʻqitish jarayonida koʻrmagan maʼlumotlarga qarab baholash muhim. Bu ishni maʼlumotlarni taqsimlash yoki ajratish usuli orqali hal qilish mumkin. Ushbu maqolada mashinali oʻqitishda maʼlumotlar toʻplamini oʻqitish, baholash va test toʻplamlariga ajratish hamda modelni samaradorlik (accuracy), aniqlik (precision), eslab qolish(recall) va F1-ball (F1 score) orqali baholash usullari keltiriladi.
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