Fit CatBoostClassifier
Implementation of the scikit-learn API for CatBoost classifier.
This algorithm creates a trained Catboost classifier. CatBoost is an ensemble learning technique that combines multiple decision trees to make predictions. It works sequentially by adding trees one by one to correct errors made by the previous ones.
Usage:
Start the algorithm from the Processing Toolbox panel.
Select a training dataset or create one by clicking the processing algorithm icon, then click run.
Parameters
- Classifier [string]
Scikit-learn python code. See CatBoostClassifier for information on different parameters. Default:
from catboost import CatBoostClassifier classifier = CatBoostClassifier\(n_estimators=100\)
- 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:FitCatboostclassifier
:
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Arguments
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classifier: Classifier
Default value: from catboost import CatBoostClassifier
classifier = CatBoostClassifier(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