Boosting nima?

Boosting — bu ansambl o‘rganish usuli bo‘lib, u ketma-ket bir nechta zaif modellarni (odatda qaror daraxtlari) o‘rgatadi, har biri avvalgilarning xatolarini to‘g‘rilaydi. Natijada, yuqori aniqlikka ega kuchli model hosil bo‘ladi. Asosiy g‘oya — har bir bosqichda avvalgi modellar xato qilgan ob’ektlarga ko‘proq e’tibor qaratish.

Misol: AdaBoost, Gradient Boosting, XGBoost — mashhur boosting amalga oshirishlari.

Python’da sklearn bilan misol:

from sklearn.ensemble import GradientBoostingClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

model = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1)
model.fit(X_train, y_train)
print(f"Aniqlik: {model.score(X_test, y_test):.2f}")