ml - Weighting Random Forest (+ cross-validation)

None

Paramètres

ListOf_Input_Features_Image: ListOf( Volume 3D ) ( input )

ListOf_Input_Segmented_Image: ListOf( Graph ) ( input )

Input_Feature_Description: Text file ( optional, entrée )

PCA: Booléen ( input )

N_Principal_Components: Entier ( optional, input )

Alpha: Réel ( optional, input )

Weights: ListOf( Réel ) ( optional, input )

Weighted_Split: Booléen ( input )

Weighted_Voting: Booléen ( input )

Cross_Validation: Booléen ( optional, input )

K_Folds: Entier ( optional, input )

N_Trees: Entier ( input )

Output_Model: Any Type ( sortie )

Label_Of_Interest: Entier ( input )

Output_OOB_Decision: Text file ( sortie )

Append_Performance: Booléen ( input )

Output_Performance: Text file ( sortie )

Informations techniques

Toolbox : Bioprocessing

Niveau d'utilisateur : 0

Identifiant : ml_weighting_random_forest

Nom de fichier : brainvisa/toolboxes/bioprocessing/processes/research/toolbox/machine_learning/ml_toolbox/ml_weighting_random_forest.py

Supported file formats :

ListOf_Input_Features_Image :

gz compressed NIFTI-1 image, Aperio svs, DICOM image, Directory, ECAT i image, ECAT v image, FDF image, FreesurferMGH, FreesurferMGZ, GIS image, Hamamatsu ndpi, Hamamatsu vms, Hamamatsu vmu, JPEG image, Leica scn, MINC image, NIFTI-1 image, SPM image, Sakura svslide, TIFF image, TIFF image, TIFF(.tif) image, TIFF(.tif) image, Ventana bif, 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

Output_OOB_Decision :

Text file, Text file

Output_Performance :

Text file, Text file