This task applies Bayes theorem with a strong assumption that all the predictors are independent to each other; i.e., the presence of a feature in a class is independent to the presence of any other feature in the same class.
For background on the algorithm used, see Naive Bayes 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('TrainNaiveBayes')
TrainTask.INPUT_RASTER = DataPrepTask.OUTPUT_RASTER
TrainTask.Execute
outputModelUri = TrainTask.OUTPUT_MODEL_URI
print, 'Model URI: ' + outputModelUri
outputModel = TrainTask.OUTPUT_MODEL
print, outputModel.Attributes, /IMPLIED
Syntax
Result = ENVITask('TrainNaiveBayes')
Input parameters (Set, Get): BALANCE_CLASSES, INPUT_RASTERS, MODEL_DESCRIPTION, MODEL_NAME, MODEL_VERSION, 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 examples.
INPUT_RASTERS (required)
Specify one or more preprocessed training rasters to be used for training.
MODEL_DESCRIPTION (optional)
Specify the purpose of the model.
MODEL_NAME (optional)
Specify the name of the model. The default is Naïve Bayes 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
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 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, TrainExtraTrees Task, TrainIsolationForest Task, TrainKNeighbors Task, TrainLinearSVM Task, TrainLocalOutlierFactor Task, TrainMiniBatchKMeans Task, TrainRandomForest Task, TrainRBFSVM Task