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:

1. Start the algorithm from the Processing Toolbox panel.

2. 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:

----------------
Arguments
----------------

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

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

outputClassifier: <outputFile>
    Output classifier