Fit CatBoostRegressor

Implementation of the scikit-learn API for CatBoost regressor.

This algorithm creates a trained Catboost regressor. CatBoost is an ensemble learning technique that combines multiple decision trees to make predictions. It works sequentially by adding trees one by one to correct errors made by the previous ones.

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 CatBoostRegressor for information on different parameters.

Default:

from catboost import CatBoostRegressor
regressor = CatBoostRegressor(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:FitCatboostregressor:

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

regressor: Regressor
    Default value:  from catboost import CatBoostRegressor
regressor = CatBoostRegressor(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

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

outputRegressor: <outputFile>
    Output regressor