Tool Random Forest Table Classification (ViGrA)

Random Forest Table Classification.

References

ViGrA - Vision with Generic Algorithms

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

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	`RF_TREE_COUNT`	How many trees to create?	Minimum: 1 Default: 32
	Samples per Tree	Floating point	`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	`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
(*) optional

Command-line

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