7 skills found
rvalavi / BlockCVThe blockCV package creates spatially or environmentally separated training and testing folds for cross-validation to provide a robust error estimation in spatially structured environments. See
SamComber / SpacvSpatial cross-validation in Python.
WalidGharianiEAGLE / Spatial Kfoldspatial resampling for more robust cross validation in spatial studies
geoai-lab / SpatialCVSpatial cross-validation for GeoAI
envima / SpatialMaxentspatialMaxent is an extension for Maxent version 3.4.4 (Copyright 2016 Steven Phillips, Miro Dudik and Rob Schapire), that provides a Forward-Variables-Selection (FVS), Forward-Feature-Selection (FFS) and tuning of the regularization multiplier together with spatial cross-validation. These methods are especially suited for spatial data.
simonkassel / R ArcGISSpatial leave-one-out cross-validation for a logistic regression with ESRI's R-ArcGIS Bridge
ldominguezruben / ASETASET is a Matlab®-based toolbox developed to obtain, visualize, and validate calibrations between the echo intensity level from static ADCP measurements and suspended sediment concentration values measured simultaneously in different points of surveyed verticals with traditional techniques. In addition, once the calibration is obtained, ASET transforms the acoustic intensity signal into sediment concentration for each cell measured by the ADCP, obtaining the same spatial resolution as the velocity field in the cross-section or temporal series (for static measurements). Extrapolation methods are also included to estimate velocity and concentration values in areas not measured by the ADCP, (i.e., near bottom, surface, and banks), with visualization modules for the user to evaluate each method. Finally, an integration module makes possible to compute the total suspended sediment concentration in a cross-section.
