Fit RandomForestClassifier
A random forest classifier. A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is controlled with the max_samples parameter if bootstrap=True (default), otherwise the whole dataset is used to build each tree.
A Random Forest classifier works by constructing a multitude of decision trees during training and then combines their predictions to make a final prediction.
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 RandomForestClassifier for information on different parameters.
Default:
from sklearn.ensemble import RandomForestClassifier classifier = RandomForestClassifier(n_estimators=100, oob_score=True)
- 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:FitRandomforestclassifier
:
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Arguments
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classifier: Classifier
Default value: from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier(n_estimators=100, oob_score=True)
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