Ko’p xususiyatli (o’zgaruvchili)
Chiziqli regressiya
Yuza(m2)
|
Nariz($1000)
|
2104
|
460
|
1416
|
232
|
1534
|
315
|
852
|
178
|
…
|
…
|
Bir xususiyatli (o’zgaruvchili) bo’lganda
Yuzasi(m2)
|
Xonalar soni
|
Qavatlar soni
|
Yoshi (yil)
|
Narxi ($1000)
|
2104
|
5
|
1
|
45
|
460
|
1416
|
3
|
2
|
40
|
232
|
1534
|
3
|
2
|
30
|
315
|
852
|
2
|
1
|
36
|
178
|
…
|
…
|
…
|
…
|
…
|
Ko’p xususiyatli (o’zgaruvchili).
Ko’p xususiyatli(o’zgaruvchili).
Belgilanadi:
= xususiyatlar nomeri
= o’zratuvchi tanlamaning i- xususiyatlari
= i- o’zratuvchi tanlamaning xususiyatlarining j- xususiyati
Yuzasi(m2)
|
Xonalar soni
|
Qavatlar soni
|
Yoshi (yil)
|
Narxi ($1000)
|
2104
|
5
|
1
|
45
|
460
|
1416
|
3
|
2
|
40
|
232
|
1534
|
3
|
2
|
30
|
315
|
852
|
2
|
1
|
36
|
178
|
…
|
…
|
…
|
…
|
…
|
Gepoteza:
Oldin:
Bu yerda deb qaralishi kerak.
Gepoteza:
Cost funksiyasi:
Parametrlar:
Takrorlash
Gradient descent:
( yaqinlashguncha)
Gradient Descent
Takrorlash
Oldingi holda(n=1):
Yangi algoritmda :
Takrorlash
( yaqinlashguncha )
Misol. = yuza(0-2000 m2)
= Xonalar soni(1-5)
Xususiyatlar shkalasi
G’oya: Turli shkaladagi xususiyatlarni baravarlashtirish.
yuza(m2)
Xonalar soni
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
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