ml - Logistic regression cross-validation

None

Paramètres

ListOf_Input_Image: ListOf( Volume 3D ) ( input )

ListOf_Input_Segmented_Image: ListOf( Graph ) ( input )

PCA: Booléen ( input )

K_folds: Entier ( input )

Performance_Metric: Choice ( input )

C_Regularization_Grid: ListOf( Réel ) ( input )

N_Principal_Components_Grid: ListOf( Entier ) ( optional, input )

Output_Performance: Text file ( optional, sortie )

Output_Best_Model: Any Type ( sortie )

N_Jobs: Entier ( input )

Informations techniques

Toolbox : Bioprocessing

Niveau d'utilisateur : 0

Identifiant : ml_Logit

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

Supported file formats :

ListOf_Input_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

Output_Performance :

Text file, Text file

Output_Best_Model :

pickle file, pickle file