geopyter, an acronym of Geographical Python Teaching Resources, provides a hub for the distribution of ‘best practice’ in computational and spa
The elemental profiles of 131 rice samples were determined, and two machine learning algorithms were implemented, support vector machines (SVM) and random forest (RF), together with the feature selection algorithm Relief. Prediction accuracy of 100% was achieved by both Relief-SVM and Relief-RF ...
Python 3.6 with Scikit-learn 0.18 package (Pedregosa et al., 2011) was used to create the RF regression models and output of the resulting of cross-validation. ArcGIS 10.4 (ESRI, 380 New York Street, Redlands, CA 92373, USA) was used to extract the pixel value of remotely sensed and ...
As mentioned above, the SDM toolbox (Tool of ArcGIS based on Python 2.7.14) was also used to treat the suitable habitats ofC. lanceolataas a whole and to reduce them to a vector particle. Then, the changing trend of the suitable habitats and the distributional core position of suitable ha...
Confidence intervals for random forests in python. J. Open Source Softw. 2017, 2, 124. [CrossRef] 66. Adugna, T.; Xu, W.; Fan, J. Comparison of Random Forest and Support Vector Machine Classifiers for Regional Land Cover Mapping Using Coarse Resolution FY-3C Images. Remote Sens. 2022...
Breiman L, 2001. Random forests.Machine Learning, 45(1): 5–32. ArticleGoogle Scholar Bui M T, Kuzovlev V V, Zhenikov Y Net al., 2018. Water temperatures in the headwaters of the Volga River: Trend analyses, possible future changes, and implications for a pan-European perspective.River...
SDMtoolbox 2.0: The next generation Python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. PeerJ 2017, 5, e4095. [Google Scholar] [CrossRef] [PubMed] [Green Version] Guisan, A.; Tingley, R.; Baumgartner, J.B.; Naujokaitis-Lewis, I.; ...