This task implements a meta estimator that fits several randomized decision trees (i.e., extra-trees) on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting.
For background on the algorithm used, see Extra Trees Classification.
Example
e = ENVI()
RasterFile = Filepath('qb_boulder_msi', Subdir=['data'], $
Root_Dir=e.Root_Dir)
Raster = e.OpenRaster(RasterFile)
ROIFile = Filepath('qb_boulder_roi.xml', Subdir=['data'], $
Root_Dir=e.Root_Dir)
ROI = e.OpenROI(ROIFile)
StatsTask = ENVITask('NormalizationStatistics')
StatsTask.INPUT_RASTERS = Raster
StatsTask.Execute
DataPrepTask = ENVITask('MLTrainingDataFromROIs')
DataPrepTask.INPUT_RASTER = Raster
DataPrepTask.INPUT_ROI = ROI
DataPrepTask.BACKGROUND_LABELS = ['Disturbed Earth', 'Water']
DataPrepTask.NORMALIZE_MIN_MAX = StatsTask.Normalization
DataPrepTask.Execute
TrainTask = ENVITask('TrainExtraTrees')
TrainTask.INPUT_RASTER = DataPrepTask.OUTPUT_RASTER
TrainTask.NUM_ESTIMATORS = 100
TrainTask.Execute
outputModelUri = TrainTask.OUTPUT_MODEL_URI
print, 'Model URI: ' + outputModelUri
outputModel = TrainTask.OUTPUT_MODEL
print, outputModel.Attributes, /IMPLIED
Syntax
Result = ENVITask('TrainExtraTrees')
Input parameters (Set, Get): BALANCE_CLASSES, CUSTOM_MAX_FEATURES, INPUT_RASTERS, MAX_DEPTH, MAX_FEATURES, MODEL_DESCRIPTION, MODEL_NAME, MODEL_VERSION, NUM_ESTIMATORS, OUTPUT_MODEL_URI, REMOVE_OUTLIERS
Output parameters (Get only): OUTPUT_MODEL
Parameters marked as "Set" are those that you can set to specific values. You can also retrieve their current values any time. Parameters marked as "Get" are those whose values you can retrieve but not set.
Input Parameters
BALANCE_CLASSES (optional)
Specify whether all classes should be considered equal during training. This helps to account for classes with few samples compared to classes with many samples.
CUSTOM_MAX_FEATURES (optional)
Specify the number of features to consider when looking for the best split. This parameter accepts a float or integer value. If specified, this value will override MAX_FEATURES.
INPUT_RASTERS (required)
Specify one or more preprocessed training rasters to be used for training.
MAX_DEPTH (optional)
Specify the number of decision trees to use. The estimators are the predictors of the algorithm. The default is 100.
MAX_FEATURES (optional)
Specify the number of features to consider when looking for the best split. This parameter offers options sqrt or log2 string literals. The default is sqrt.
MODEL_DESCRIPTION (optional)
Specify the purpose of the model.
MODEL_NAME (optional)
Specify the name of the model. The default is Extra Trees Supervised Classifier.
MODEL_VERSION (optional)
Specify a semantic version format (MAJOR.MINOR.PATCH) for the trained model (for example, 1.0.0). The version may indicate the following:
- MAJOR: Breaking changes to the model
- MINOR: Compatibility or new features
- PATCH: Minor adjustments
NUM_ESTIMATORS (optional)
Specify the number of decision trees in the forest. The estimators are the predictors of the algorithm. The default is 100.
OUTPUT_MODEL_URI (optional)
Specify a string with the fully qualified filename and path of the associated OUTPUT_MODEL. If you do not specify this parameter, or set it to an exclamation symbol (!), a temporary file will be created.
REMOVE_OUTLIERS (optional)
The default of true removes outliers using histogram stretching to increase the minimum and decrease the maximum data values. If set to false, use the true minimum and maximum data ranges to normalize the data.
Output Parameters
OUTPUT_MODEL
This is a reference to the output model file.
Methods
Execute
Parameter
ParameterNames
See ENVI Help for details on these ENVITask methods.
Properties
DESCRIPTION
DISPLAY_NAME
NAME
REVISION
See the ENVITask topic in ENVI Help for details.
Version History
Machine Learning 2.0
|
Introduced |
Machine Learning 2.1
|
Added MAX_FEATURES and CUSTOM_MAX_FEATURES parameters
|
Machine Learning 3.0.1
|
Added REMOVE_OUTLIERS parameter
|
Machine Learning 6.2
|
Added the MODEL_VERSION and OUTPUT_MODEL_URI parameters
|
See Also
ENVI Machine Learning Algorithms Background, TrainBirch Task, TrainIsolationForest Task, TrainKNeighbors Task, TrainLinearSVM Task, TrainLocalOutlierFactor Task, TrainMiniBatchKMeans Task, TrainNaiveBayes Task, TrainRandomForest Task, TrainRBFSVM Task