Fit XGBClassifier

Implementation of the scikit-learn API for XGBoost classification.

An XGBoost (Extreme Gradient Boosting) classifier is an ensemble learning algorithm based on decision trees.XGBoost builds an ensemble of decision trees and uses gradient boosting to improve model accuracy.

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

Classifier [string]

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

Default:

from xgboost import XGBClassifier
classifier = XGBClassifier(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:FitXgbclassifier:

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

classifier: Classifier
    Default value:  from xgboost import XGBClassifier
classifier = XGBClassifier(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