ml - Fast model generation and testing

None

Parameters

ListOf_Input_Image: ListOf( 3D Volume ) ( input )

ListOf_Input_Segmented_Image: ListOf( Graph ) ( input )

Algorithm: Choice ( input )

Test_Size_Ratio: Float ( input )

PCA: Boolean ( input )

N_Principal_Components: Integer ( optional, input )

Regularization_Parameter: Float ( optional, input )

N_Random_Trees: Integer ( optional, input )

Output_Performance: Text file ( optional, output )

Output_Subset_Data: Text file ( optional, output )

Output_Model: Any Type ( optional, output )

ListOf_Output_Segmented_Image: ListOf( 3D Volume ) ( optional, output )

Technical information

Toolbox : Bioprocessing

User level : 0

Identifier : ml_fast_modeling

File name : brainvisa/toolboxes/bioprocessing/processes/research/toolbox/machine_learning/ml_fast_modeling.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_Subset_Data :

Text file, Text file

Output_Model :

pickle file, pickle file

ListOf_Output_Segmented_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