underfitting va overfitting muammolari

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prezentatsiya powerpoint underfitting va overfitting muammolari ma’ruza rejasi underfitting va overfitting tushunchasi haddan tashqari mos keladiganlarga murojaat qilish cost funksiyada sozlash parametrini qo’shish chiziqli regressiyada sozlash masalasi logistik regressiyada sozlash masalasi sozlash parametrini tanlash misol: chiziqli regressiya (uy narxi) overfitting: agar bizda juda ko'p xususiyatlar mavjud bo'lsa, o'rganilgan model training to'plamiga juda mos kelishi mumkin ( ), ammo yangi misollarni aniqlashda xatolik kata bo’ladi. narxi yuzasi narxi yuzasi narxi yuzasi 42.940038684719497 72.340425531914903 110.251450676983 195.357833655706 304.44874274661498 386.46034816247601 45.173745173745203 137.83783783783801 234.362934362934 274.517374517375 291.50579150579199 303.08880308880299 42.940038684719497 72.340425531914903 110.251450676983 195.357833655706 304.44874274661498 386.46034816247601 45.173745173745203 137.83783783783801 234.362934362934 274.517374517375 291.50579150579199 303.08880308880299 42.940038684719497 72.340425531914903 110.251450676983 195.357833655706 304.44874274661498 386.46034816247601 45.173745173745203 137.83783783783801 234.362934362934 274.517374517375 291.50579150579199 303.08880308880299 misol: logistik regressiya ( = sigmoid funksiyasi) x1 x2 x1 x2 x1 x2 bias va variance tushunchalari. bias - bu bizning modelimizning o'rtacha bashorati va biz taxmin qilmoqchi bo'lgan to'g'ri qiymat o'rtasidagi farq. yuqori darajadagi model o'quv ma'lumotlariga juda kam e'tibor beradi va modelni …

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da yuqori xato ko'rsatkichlariga ega. bias va variance tushunchalari. foydalanilgan adabiyotlar aurelian geron, hands on machine learning with scikit-learn keras&tensorflow // second edition concepts, tools, and techniques to build intelligent systems, 2019, 510 pages https://www.geeksforgeeks.org/ml-types-learning-supervised-learning/ https://www.guru99.com/unsupervised-machine-learning.html https://www.w3schools.com/python/python_ml_linear_regression.asp https://www.w3schools.com/python/python_ml_multiple_regression.asp https://www.w3schools.com/python/python_ml_polynomial_regression.asp https://www.mathworks.com/help/stats/regress.html image1.png image5.png image6.emf image2.png image3.png image4.png image11.emf image6.png image7.png image8.png image9.png image10.png

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prezentatsiya powerpoint underfitting va overfitting muammolari ma’ruza rejasi underfitting va overfitting tushunchasi haddan tashqari mos keladiganlarga murojaat qilish cost funksiyada sozlash parametrini qo’shish chiziqli regressiyada sozlash masalasi logistik regressiyada sozlash masalasi sozlash parametrini tanlash misol: chiziqli regressiya (uy narxi) overfitting: agar bizda juda ko'p xususiyatlar mavjud bo'lsa, o'rganilgan model training to'plamiga juda mos kelishi mumkin ( ), ammo yangi misollarni aniqlashda xatolik kata bo’ladi. narxi yuzasi narxi yuzasi narxi yuzasi 42.940038684719497 72.340425531914903 110.251450676983 195.357833655706 304.44874274661498 386.46034816247601 45.173745173745203 137.83783783783801 234.362934362934 274.517374517375 291.50579150579199 303.0888030888029...

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