Methoden vergleichen

	Geographically Weighted Random Forest	Random Forest
Fachgebiet	Räumliche Analyse	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2021	2001
Urheber	Stefanos Georganos et al.	Breiman, L.
Typ	Spatially local ensemble learning method	Ensemble (bagging of decision trees)
Wegweisende Quelle	Georganos, S., et al. (2021). Geographical random forests: a spatial extension of the random forest algorithm. Geocarto International, 36(2), 121–136.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Aliasnamen	Geographical Random Forest, GRF, Spatial Random Forest, Cografi Agirlikli Rastgele Orman	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Verwandt	3	4
Zusammenfassung	Geographically Weighted Random Forest (GWRF) is a spatially local ensemble learning method that fits an independent Random Forest model at each observation location, weighting nearby training samples more heavily than distant ones through a spatial kernel function. It was introduced by Stefanos Georganos and colleagues in 2019 (published 2021) as an extension of Breiman's Random Forest to handle spatial non-stationarity — the phenomenon where predictor–response relationships vary across geographic space.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.