Method save_model

catboost/python-package/catboost/core.py:3702–3743

Save the model to a file. Parameters ---------- fname : string Output file name. format : string Possible values: * 'cbm' for catboost binary format, * 'coreml' to export into Apple CoreML format

(self, fname, format="cbm", export_parameters=None, pool=None)

        self._base_drop_unused_features()

    def save_model(self, fname, format="cbm", export_parameters=None, pool=None):
        """
        Save the model to a file.

        Parameters
        ----------
        fname : string
            Output file name.
        format : string
            Possible values:
                * 'cbm' for catboost binary format,
                * 'coreml' to export into Apple CoreML format
                * 'onnx' to export into ONNX-ML format
                * 'pmml' to export into PMML format
                * 'cpp' to export as C++ code
                * 'python' to export as Python code.
        export_parameters : dict
            Parameters for CoreML export:
                * prediction_type : string - either 'probability' or 'raw'
                * coreml_description : string
                * coreml_model_version : string
                * coreml_model_author : string
                * coreml_model_license: string
            Parameters for PMML export:
                * pmml_copyright : string
                * pmml_description : string
                * pmml_model_version : string
        pool : catboost.Pool or list or numpy.ndarray or pandas.DataFrame or pandas.Series or polars.DataFrame or catboost.FeaturesData
            Training pool.
        """
        if not self.is_fitted():
            raise CatBoostError("There is no trained model to use save_model(). Use fit() to train model. Then use this method.")
        if not isinstance(fname, PATH_TYPES):
            raise CatBoostError("Invalid fname type={}: must be str or os.PathLike.".format(type(fname)))
        if pool is not None and not isinstance(pool, Pool):
            pool = Pool(
                data=pool,
                cat_features=self._get_cat_feature_indices() if not isinstance(pool, FeaturesData) else None,
                text_features=self._get_text_feature_indices() if not isinstance(pool, FeaturesData) else None,
                embedding_features=self._get_embedding_feature_indices() if not isinstance(pool, FeaturesData) else None
            )
        self._save_model(fname, format, export_parameters, pool)

    def load_model(self, fname=None, format='cbm', stream=None, blob=None):
        """