Fit XGBRegressor

Implementation of the scikit-learn API for XGBoost regression.

XGBoost is a powerful and versatile algorithm for regression tasks. It is well suited to handle large datasets with high-dimensional feature spaces.

Usage:

1. Start the algorithm from the Processing Toolbox panel.

2. Select a training dataset or create one by clicking the processing algorithm icon, then click run.

   

Parameters

Regressor [string]

Scikit-learn python code. See XGBRegressor for information on different parameters.

Default:

from xgboost import XGBRegressor

regressor = XGBRegressor(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 regressor [fileDestination]

Pickle file destination.

Command-line usage

>qgis_process help enmapbox:FitXgbregressor:

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

regressor: Regressor
    Default value:  from xgboost import XGBRegressor

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

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

outputRegressor: <outputFile>
    Output regressor