SAGA-GIS Tool Library Documentation (v7.7.0)

Tool Decision Tree Classification (OpenCV)

Integration of the OpenCV Machine Learning library for Decision Tree classification of gridded features.


References


Parameters

 NameTypeIdentifierDescriptionConstraints
InputFeaturesGrid list, inputFEATURES--
Training AreasShapes, inputTRAIN_AREAS--
OutputClassificationGrid, outputCLASSES--
OptionsGrid SystemGrid systemPARAMETERS_GRID_SYSTEM--
NormalizeBooleanNORMALIZE-Default: 0
Update Colors from FeaturesBoolean, GUIRGB_COLORSUse the first three features in list to obtain blue, green, red components for class colour in look-up table.Default: 1
Load ModelFile pathMODEL_LOADUse a model previously stored to file.-
Class IdentifierTable fieldTRAIN_CLASS--
Buffer SizeFloating pointTRAIN_BUFFERFor non-polygon type training areas, creates a buffer with a diameter of specified size.Minimum: 0.000000
Default: 1.000000
Save ModelFile pathMODEL_SAVEStores model to file to be used for subsequent classifications instead of training areas.-
Maximum Tree DepthIntegerMAX_DEPTHThe maximum possible depth of the tree. That is the training algorithms attempts to split a node while its depth is less than maxDepth. The root node has zero depth.Minimum: 1
Default: 10
Minimum Sample CountIntegerMIN_SAMPLESIf the number of samples in a node is less than this parameter then the node will not be split.Minimum: 2
Default: 2
Maximum CategoriesIntegerMAX_CATEGRSCluster possible values of a categorical variable into K<=maxCategories clusters to find a suboptimal split.Minimum: 1
Default: 10
Use 1SE RuleBoolean1SE_RULEIf true then a pruning will be harsher. This will make a tree more compact and more resistant to the training data noise but a bit less accurate.Default: 1
Truncate Pruned TreesBooleanTRUNC_PRUNEDIf true then pruned branches are physically removed from the tree. Otherwise they are retained and it is possible to get results from the original unpruned (or pruned less aggressively) tree.Default: 1
Regression AccuracyFloating pointREG_ACCURACYTermination criteria for regression trees. If all absolute differences between an estimated value in a node and values of train samples in this node are less than this parameter then the node will not be split further.Minimum: 0.000000
Default: 0.010000

Command-line

Usage: saga_cmd imagery_opencv 8 [-FEATURES <str>] [-NORMALIZE <str>] [-CLASSES <str>] [-MODEL_LOAD <str>] [-TRAIN_AREAS <str>] [-TRAIN_CLASS <str>] [-TRAIN_BUFFER <double>] [-MODEL_SAVE <str>] [-MAX_DEPTH <num>] [-MIN_SAMPLES <num>] [-MAX_CATEGRS <num>] [-1SE_RULE <str>] [-TRUNC_PRUNED <str>] [-REG_ACCURACY <double>]
  -FEATURES:<str>       	Features
	Grid list, input
  -NORMALIZE:<str>      	Normalize
	Boolean
	Default: 0
  -CLASSES:<str>        	Classification
	Grid, output
  -MODEL_LOAD:<str>     	Load Model
	File path
  -TRAIN_AREAS:<str>    	Training Areas
	Shapes, input
  -TRAIN_CLASS:<str>    	Class Identifier
	Table field
  -TRAIN_BUFFER:<double>	Buffer Size
	Floating point
	Minimum: 0.000000
	Default: 1.000000
  -MODEL_SAVE:<str>     	Save Model
	File path
  -MAX_DEPTH:<num>      	Maximum Tree Depth
	Integer
	Minimum: 1
	Default: 10
  -MIN_SAMPLES:<num>    	Minimum Sample Count
	Integer
	Minimum: 2
	Default: 2
  -MAX_CATEGRS:<num>    	Maximum Categories
	Integer
	Minimum: 1
	Default: 10
  -1SE_RULE:<str>       	Use 1SE Rule
	Boolean
	Default: 1
  -TRUNC_PRUNED:<str>   	Truncate Pruned Trees
	Boolean
	Default: 1
  -REG_ACCURACY:<double>	Regression Accuracy
	Floating point
	Minimum: 0.000000
	Default: 0.010000