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Showing content from https://catboost.ai/docs/concepts/python-reference_catboost_save_model.html below:

save_model | CatBoost

Save the model to a file.

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

The path to the output model.

Possible types

string

Default value

Required parameter

The output format of the model.

Possible values:

• cbm — CatBoost binary format.

• coreml — Apple CoreML format (only datasets without categorical features are currently supported).

• json — JSON format. Refer to the CatBoost JSON model tutorial for format details.

• python — Standalone Python code. See the Python section for details on applying the resulting model.

• cpp — Standalone C++ code. See the C++ section for details on applying the resulting model.

• onnx — ONNX-ML format (only datasets without categorical features are currently supported). Refer to https://onnx.ai/ for details. See the ONNX section for details on applying the resulting model.

• pmml — PMML version 4.3 format. Categorical features must be interpreted as one-hot encoded during the training if present in the training dataset. This can be accomplished by setting the --one-hot-max-size/one_hot_max_size parameter to a value that is greater than the maximum number of unique categorical feature values among all categorical features in the dataset. See the PMML section for details on applying the resulting model.

  Note

  Multiclassification models are not currently supported.

Possible types

string

Default value

cbm

Additional format-dependent parameters for:

• Apple CoreML

  Possible values (all are strings):

  • prediction_type. Possible values are probability and raw.

  • coreml_description

  • coreml_model_version

  • coreml_model_author

  • coreml_model_license

• ONNX-ML

  • onnx_graph_name
  • onnx_domain
  • onnx_model_version
  • onnx_doc_string

  See the ONNX-ML parameters reference for details.

• PMML

  Possible values (all are strings):

  • pmml_copyright
  • pmml_description
  • pmml_model_version

  See the PMML parameters reference for details.

Possible types

dict

Default value

None

The dataset previously used for training.

This parameter is required if the model contains categorical features and the output format is cpp, python, or JSON.

Note

The model can be saved to the JSON format without a pool. In this case it is available for review but it is not applicable.

Possible types

• catboost.Pool
• list
• numpy.ndarray
• pandas.DataFrame
• pandas.Series
• catboost.FeaturesData

Default value
None

• Python
• C++
• CoreML
• ONNX
• Export a model to JSON

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