---
metadata:
  - name: generator
    content: Diplodoc Platform v5.52.0
alternate:
  - https://catboost.ai/docs/en/concepts/export-coreml.md
  - href: en/concepts/export-coreml.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

# CoreML

<!-- source: en/_includes/work_src/reusage-python/core-mle__intro.md -->
Trained CatBoost models can be exported to CoreML.
<!-- endsource: en/_includes/work_src/reusage-python/core-mle__intro.md -->


The following example showcases how to train a model using [CatBoostClassifier](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier.md), save it CoreML using the [save_model](https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_save_model.md) function and import the model to Xcode:

1. Train the model and save it in CoreML format.

    For example, if training on the Iris dataset:
    ```python
    import catboost
    import sklearn

    iris = sklearn.datasets.load_iris()
    cls = catboost.CatBoostClassifier(loss_function='MultiClass')

    cls.fit(iris.data, iris.target)

    # Save model to catboost format
    cls.save_model("iris.mlmodel", format="coreml", export_parameters={'prediction_type': 'probability'})
    ```

1. Import the resulting model to Xcode.

    The following is an example of importing with Swift:
    ```python
    import CoreML

    let model = iris()
    let sepal_l = 7.0
    let sepal_w = 3.2
    let petal_l = 4.7
    let petal_w = 1.4

    guard let output = try? model.prediction(input: irisInput(feature_0: sepal_l, feature_1: sepal_w, feature_2: petal_l, feature_3: petal_w)) else {
    fatalError("Unexpected runtime error.")
    }

    print(String(
    format: "Output probabilities: %1.5f; %1.5f; %1.5f",
    output.prediction[0].doubleValue,
    output.prediction[1].doubleValue,
    output.prediction[2].doubleValue
    ))
    ```