ml - Weighted Random Forest

None

Parameters

ListOf_Input_Features_Image: ListOf( 3D Volume ) ( input )

ListOf_Input_Segmented_Image: ListOf( Graph ) ( input )

Input_Feature_Description: Text file ( optional, input )

Classes_Weights: ListOf( Float ) ( optional, input )

Max_Depth: Integer ( optional, input )

N_Trees: Integer ( input )

Output_Model: Any Type ( output )

Technical information

Toolbox : Bioprocessing

User level : 3

Identifier : ml_RandomForest

File name : brainvisa/toolboxes/bioprocessing/processes/research/toolbox/machine_learning/ml_toolbox/ml_RandomForest.py

Supported file formats :

ListOf_Input_Features_Image :

gz compressed NIFTI-1 image, Aperio svs, BMP image, DICOM image, Directory, ECAT i image, ECAT v image, FDF image, FreesurferMGH, FreesurferMGZ, GIF image, GIS image, Hamamatsu ndpi, Hamamatsu vms, Hamamatsu vmu, JPEG image, Leica scn, MINC image, NIFTI-1 image, PBM image, PGM image, PNG image, PPM image, SPM image, Sakura svslide, TIFF image, TIFF image, TIFF(.tif) image, TIFF(.tif) image, VIDA image, Ventana bif, XBM image, XPM image, Zeiss czi, gz compressed MINC image, gz compressed NIFTI-1 image

ListOf_Input_Segmented_Image :

Graph and data, Graph and data

Input_Feature_Description :

Text file, Text file

Output_Model :

pickle file, pickle file