<jupyter_start><jupyter_text>CatBoost and CoreML tutorial — Iris dataset CatBoost does support model export to Apple's [CoreML](https://developer.apple.com/machine-learning/) format, which lets you to easily embed ML models into applications on Apple's platforms.Due to limitations of CoreML spec, export of models with categorical features is not currently supported.This tutorial demonstrates exporting of CatBoost model trained on  [Iris](https://en.wikipedia.org/wiki/Iris_flower_data_set) dataset to CoreML model. Get iris dataset:<jupyter_code>from sklearn import datasets
iris = datasets.load_iris()<jupyter_output><empty_output><jupyter_text>Train the model:<jupyter_code>import catboost
cls = catboost.CatBoostClassifier(loss_function='MultiClass')
cls.fit(iris.data, iris.target)<jupyter_output><empty_output><jupyter_text>Predict probabilities:<jupyter_code>cls.predict(iris.data[0:1], prediction_type="Probability")<jupyter_output><empty_output><jupyter_text>Save CoreML model:<jupyter_code>cls.save_model(
    "iris.mlmodel",
    format="coreml", 
    export_parameters={
        'prediction_type': 'probability'
    }
)<jupyter_output><empty_output>
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