SAGA 9.4.2 | Tool Library Documentation

Random Forest Table Classification (ViGrA)


Description

Random Forest Table Classification.


References


Parameters

 NameTypeIdentifierDescriptionConstraints
InputTabletable, inputTABLETable with features, must include class-ID-
OutputFeature Importancestable, outputIMPORTANCES--
OptionsFeaturestable fieldsFEATURESSelect features (table fields) for classification-
Predictiontable fieldPREDICTIONThis is field that will have the prediction results. If not set it will be added to the table.-
Trainingtable fieldTRAININGthis is the table field that defines the training classes-
Use Label as IdentifierbooleanLABEL_AS_IDUse training area labels as identifier in classification result, assumes all label values are integer numbers!Default: 0
Tree Countinteger numberRF_TREE_COUNTHow many trees to create?Minimum: 1 Default: 32
Samples per Treefloating point numberRF_TREE_SAMPLESSpecifies the fraction of the total number of samples used per tree for learning.Minimum: 0.000000 Maximum: 1.000000 Default: 1.000000
Sample with ReplacementbooleanRF_REPLACESample from training population with or without replacement?Default: 1
Minimum Node Split Sizeinteger numberRF_SPLIT_MIN_SIZENumber of examples required for a node to be split. Choose 1 for complete growing.Minimum: 1 Default: 1
Features per NodechoiceRF_NODE_FEATURES-Available Choices: [0] logarithmic [1] square root [2] all Default: 1
StratificationchoiceRF_STRATIFICATIONSpecifies 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