Fit XGBRFClassifier
Implementation of the scikit-learn API for XGBoost random forest classification.
The XGBRF classifier works by incorporating random forests into the XG Boost algorithm.
Usage:
Start the algorithm from the Processing Toolbox panel.
Select a test dataset or create one by clicking the processing algorithm icon, then click run.
Parameters
- Classifier [string]
Scikit-learn python code. See XGBRFClassifier for information on different parameters.
Default:
from xgboost import XGBRFClassifier classifier = XGBRFClassifier(n_estimators=100)
- Training dataset [file]
Training dataset pickle file used for fitting the classifier. If not specified, an unfitted classifier is created.
Outputs
- Output classifier [fileDestination]
Pickle file destination.
Command-line usage
>qgis_process help enmapbox:FitXgbrfclassifier
:
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Arguments
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classifier: Classifier
Default value: from xgboost import XGBRFClassifier
classifier = XGBRFClassifier(n_estimators=100)
Argument type: string
Acceptable values:
- String value
- field:FIELD_NAME to use a data defined value taken from the FIELD_NAME field
- expression:SOME EXPRESSION to use a data defined value calculated using a custom QGIS expression
dataset: Training dataset (optional)
Argument type: file
Acceptable values:
- Path to a file
outputClassifier: Output classifier
Argument type: fileDestination
Acceptable values:
- Path for new file
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Outputs
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outputClassifier: <outputFile>
Output classifier