Fit LGBMRegressor

Implementation of the scikit-learn API for LightGBM regressor.

LGBM is a powerful and efficient algorithm for regression tasks. It is well-suited for a wide range of applications, including problems with large datasets and high-dimensional feature spaces. When using LGBM, it is essential to fine-tune hyperparameters and monitor the training process to achieve the best model performance.

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 LGBMRegressor for information on different parameters.

Default:

from lightgbm import LGBMRegressor

regressor = LGBMRegressor(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:FitLgbmregressor:

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

regressor: Regressor
    Default value:  from lightgbm import LGBMRegressor

regressor = LGBMRegressor(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