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sklearn: KNeighborsRegressor

Régression par plus proches voisins.

python ml #sklearn#knn#regression

objectif

Régression par plus proches voisins.

code minimal

from sklearn.neighbors import KNeighborsRegressor
from sklearn.datasets import load_boston
import numpy as np
# fallback: synthèse si dataset indisponible
try:
    X, y = load_boston(return_X_y=True)
except Exception:
    rng = np.random.default_rng(0); X = rng.normal(size=(100,3)); y = X[:,0]*2 + rng.normal(size=100)
knn = KNeighborsRegressor(n_neighbors=3).fit(X, y)
print(len(knn.predict(X[:2])) == 2)

utilisation

print(knn.score(X, y) <= 1.0)

variante(s) utile(s)

from sklearn.neighbors import KNeighborsRegressor
print(hasattr(KNeighborsRegressor(weights="distance"), "fit"))

notes

  • Normaliser les features pour distances cohérentes.