Random Forest Table Classification (ViGrA)

Author: B. Bechtel, O.Conrad (c) 2015
Menu: Table | Tools

Description

Random Forest Table Classification.

References

ViGrA - Vision with Generic Algorithms

Parameters

	Name	Type	Identifier	Description	Constraints
Input	Table	table, input	`TABLE`	Table with features, must include class-ID	-
Output	Feature Importances	table, output	`IMPORTANCES`	-	-
Options	Features	table fields	`FEATURES`	Select features (table fields) for classification	-
	Prediction	table field	`PREDICTION`	This is field that will have the prediction results. If not set it will be added to the table.	-
	Training	table field	`TRAINING`	this is the table field that defines the training classes	-
	Use Label as Identifier	boolean	`LABEL_AS_ID`	Use training area labels as identifier in classification result, assumes all label values are integer numbers!	Default: 0
	Tree Count	integer number	`RF_TREE_COUNT`	How many trees to create?	Minimum: 1 Default: 32
	Samples per Tree	floating point number	`RF_TREE_SAMPLES`	Specifies the fraction of the total number of samples used per tree for learning.	Minimum: 0.000000 Maximum: 1.000000 Default: 1.000000
	Sample with Replacement	boolean	`RF_REPLACE`	Sample from training population with or without replacement?	Default: 1
	Minimum Node Split Size	integer number	`RF_SPLIT_MIN_SIZE`	Number of examples required for a node to be split. Choose 1 for complete growing.	Minimum: 1 Default: 1
	Features per Node	choice	`RF_NODE_FEATURES`	-	Available Choices: [0] logarithmic [1] square root [2] all Default: 1
	Stratification	choice	`RF_STRATIFICATION`	Specifies stratification strategy. Either none, equal amount of class samples, or proportional to fraction of class samples.	Available Choices: [0] none [1] equal [2] proportional Default: 0

Command Line

Usage: saga_cmd imagery_vigra 11 [-TABLE ] [-FEATURES ] [-PREDICTION ] [-TRAINING ] [-LABEL_AS_ID ] [-IMPORTANCES ] [-RF_TREE_COUNT ] [-RF_TREE_SAMPLES ] [-RF_REPLACE ] [-RF_SPLIT_MIN_SIZE ] [-RF_NODE_FEATURES ] [-RF_STRATIFICATION ]
  -TABLE:             	Table
	table, input
  -FEATURES:          	Features
	table fields
  -PREDICTION:        	Prediction
	table field
  -TRAINING:          	Training
	table field
  -LABEL_AS_ID:       	Use Label as Identifier
	boolean
	Default: 0
  -IMPORTANCES:       	Feature Importances
	table, output
  -RF_TREE_COUNT:     	Tree Count
	integer number
	Minimum: 1
	Default: 32
  -RF_TREE_SAMPLES:	Samples per Tree
	floating point number
	Minimum: 0.000000
	Maximum: 1.000000
	Default: 1.000000
  -RF_REPLACE:        	Sample with Replacement
	boolean
	Default: 1
  -RF_SPLIT_MIN_SIZE: 	Minimum Node Split Size
	integer number
	Minimum: 1
	Default: 1
  -RF_NODE_FEATURES:  	Features per Node
	choice
	Available Choices:
	[0] logarithmic
	[1] square root
	[2] all
	Default: 1
  -RF_STRATIFICATION: 	Stratification
	choice
	Available Choices:
	[0] none
	[1] equal
	[2] proportional
	Default: 0