CoreML

Trained CatBoost models can be exported to CoreML.

The following example showcases how to train a model using CatBoostClassifier, save it CoreML using the save_model 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:

   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'})
   

2. Import the resulting model to Xcode.

   The following is an example of importing with Swift:

   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
   ))