Train XGBoost on Zarr Data with Cross-Validation

A solution that trains an XGBoost model using data from a Zarr zip store, filters runs with only one label, and performs 10-fold cross-validation.

Arguments

--input_zarr_path

Path to the input Zarr zip store containing the features and labels. (default value: PARAMETER_VALUE)

--output_model_path

Path for the output joblib file containing the trained XGBoost model. (default value: PARAMETER_VALUE)

--n_estimators

Number of trees in the XGBoost model. (default value: 750)

--max_depth

The maximum depth of the trees. (default value: 18)

--learning_rate

The learning rate. (default value: 0.1)

--class_weights

Class weights for the XGBoost model as a comma-separated list. (default value: )

Please follow this link for details on how to install and run this solution.