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.

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

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
            - 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
outputRegressor: Output regressor
    Argument type:  fileDestination
    Acceptable values:
            - Path for new file

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Outputs
----------------

outputRegressor: <outputFile>
    Output regressor