--- metadata: - name: generator content: Diplodoc Platform v5.54.5 alternate: - https://catboost.ai/docs/en/concepts/cli-reference_normalize-model.md - href: en/concepts/cli-reference_normalize-model.md type: text/markdown title: Markdown version - href: ../llms.txt type: text/markdown title: llms.txt --- > **Documentation Index:** Fetch the complete configuration index at https://catboost.ai/docs/en/llms.txt # Scale and bias ## Purpose {#purpose} Set and/or print the model scale and bias. ## Execution format {#execution-format} ```bash catboost normalize-model [optional parameters] ``` ## Options {#options} ### -m, --model-file, --model-path #### Description The name of the input file with the description of the model obtained as the result of training. **Default value** model.bin ### --model-format #### Description The format of the input model. Possible values: - CatboostBinary. - AppleCoreML (only datasets without categorical features are currently supported). - json (multiclassification models are not currently supported). Refer to the [CatBoost JSON model tutorial](https://github.com/catboost/tutorials/blob/master/model_analysis/model_export_as_json_tutorial.ipynb) for format details. - onnx — ONNX-ML format (only datasets without categorical features are currently supported). Refer to [https://onnx.ai](https://onnx.ai) for details. See the [ONNX](https://catboost.ai/docs/en/concepts/apply-onnx-ml.md) section for details on applying the resulting model. - pmml — [PMML version 4.3](http://dmg.org/pmml/pmml-v4-3.html) 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](apply-pmml.html) section for details on applying the resulting model. **Default value** CatboostBinary ### --column-description, --cd #### Description The path to the input file that contains the [columns description](https://catboost.ai/docs/en/concepts/input-data_column-descfile.md). **Default value** If omitted, it is assumed that the first column in the file with the dataset defines the label value, and the other columns are the values of numerical features. ### --delimiter #### Description The delimiter character used to separate the data in the dataset input file. Only single char delimiters are supported. If the specified value contains more than one character, only the first one is used. {% note info %} Used only if the dataset is given in the [Delimiter-separated values format](https://catboost.ai/docs/en/concepts/input-data_values-file.md). {% endnote %} **Default value** The input data is assumed to be tab-separated ### --has-header #### Description Read the column names from the first line of the dataset file if this parameter is set. {% note info %} Used only if the dataset is given in the [Delimiter-separated values format](https://catboost.ai/docs/en/concepts/input-data_values-file.md). {% endnote %} **Default value** False (the first line is supposed to have the same data as the rest of them) ### --set-scale #### Description The model scale. **Default value** 1 ### --set-bias #### Description The model bias. The model prediction results are calculated as follows: The value of this parameters affects the prediction by changing the default value of the bias. **Default value** Depends on the value of the `--boost-from-average` for the Command-line version parameter: - True — The best constant value for the specified loss function - False — 0 ### --print-scale-and-bias #### Description Return the scale and bias of the model. These values affect the results of applying the model, since the model prediction results are calculated as follows: Scale and bias are not output **Default value** Set the scale and bias to 0.8: ### --logging-level #### Description The logging level to output to stdout. Possible values: - Silent — Do not output any logging information to stdout. - Verbose — Output the following data to stdout: - optimized metric - elapsed time of training - remaining time of training - Info — Output additional information and the number of trees. - Debug — Output debugging information. **Default value** Info ### -T, --thread-count #### Description The number of threads to use. **Default value** 4 ### --input-path #### Description The name of the input file with the [dataset](https://catboost.ai/docs/en/concepts/input-data_values-file.md#input-data_values-file). **Default value** input.tsv ### --output-model #### Description The path to the output model. **Default value** model.bin ### --output-model-format #### Description The format of the output model. Possible values: - CatboostBinary. - AppleCoreML (only datasets without categorical features are currently supported). - json (multiclassification models are not currently supported). Refer to the [CatBoost JSON model tutorial](https://github.com/catboost/tutorials/blob/master/model_analysis/model_export_as_json_tutorial.ipynb) for format details. - onnx — ONNX-ML format (only datasets without categorical features are currently supported). Refer to https://onnx.ai for details. See the [ONNX](https://catboost.ai/docs/en/concepts/apply-onnx-ml.md) section for details on applying the resulting model. - pmml — [PMML version 4.3](http://dmg.org/pmml/pmml-v4-3.html) 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](apply-pmml.html) section for details on applying the resulting model. **Default value** CatboostBinary ## Usage examples {#usage-examples} ```python catboost normalize-model --set-scale 0.8 --set-bias 0.8 --print-scale-and-bias ``` The output of this example: ```bash Input model scale 1 bias 1.405940652 Output model scale 0.8 bias 0.8 ```