Fit RandomForestRegressor
A random forest regressor. A random forest is a meta estimator that fits a number of classifying decision trees 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.
Parameters
- Regressor [string]
Scikit-learn python code. See RandomForestRegressor for information on different parameters.
Default:
from sklearn.ensemble import RandomForestRegressor regressor = RandomForestRegressor(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 regressor [fileDestination]
Pickle file destination.
Command-line usage
>qgis_process help enmapbox:FitRandomforestregressor
:
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Arguments
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regressor: Regressor
Default value: from sklearn.ensemble import RandomForestRegressor
regressor = RandomForestRegressor(n_estimators=100, oob_score=True)
Argument type: string
Acceptable values:
- String value
dataset: Training dataset (optional)
Argument type: file
Acceptable values:
- Path to a file
outputRegressor: Output regressor
Argument type: fileDestination
Acceptable values:
- Path for new file
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Outputs
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outputRegressor: <outputFile>
Output regressor