bigframes.ml.imported.XGBoostModel

class bigframes.ml.imported.XGBoostModel(model_path: str, *, input: Mapping[str, str] | None = None, output: Mapping[str, str] | None = None, session: Session | None = None)

Imported XGBoost model.

Warning

Imported XGBoost models have the several limitations. See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-xgboost#limitations

Parameters:

• model_path (str) – Cloud Storage path that holds the model files.

• input (Dict, default None) – Specify the model input schema information when you create the XGBoost model. The input should be the format of {field_name: field_type}. Input is optional only if feature_names and feature_types are both specified in the model file. Supported types are “bool”, “string”, “int64”, “float64”, “array<bool>”, “array<string>”, “array<int64>”, “array<float64>”.

• output (Dict, default None) – Specify the model output schema information when you create the XGBoost model. The input should be the format of {field_name: field_type}. Output is optional only if feature_names and feature_types are both specified in the model file. Supported types are “bool”, “string”, “int64”, “float64”, “array<bool>”, “array<string>”, “array<int64>”, “array<float64>”.

• session (BigQuery Session) – BQ session to create the model.

Methods

__init__(model_path, *[, input, output, session])
get_params([deep])	Get parameters for this estimator.
predict(X)	Predict the result from input DataFrame.
register([vertex_ai_model_id])	Register the model to Vertex AI.
to_gbq(model_name[, replace])	Save the model to BigQuery.