CatBoost Model Prediction Function

This function uses the CatBoost library to predict outcomes based on input data. It first loads a CatBoost classifier model, then converts the input data to a NumPy array, and creates a Pool object. Finally, it uses the model to predict the data and returns the predictions.

Technology Stack : CatBoost, NumPy

Code Type : Function

Code Difficulty : Intermediate

                
                    
def predict_with_catboost(model, data):
    """
    Use a CatBoost model to predict outcomes based on input data.
    """
    import numpy as np
    from catboost import CatBoostClassifier, Pool

    # Load the model
    model = CatBoostClassifier()

    # Assuming the data is a NumPy array
    data = np.array(data)

    # Create a Pool object from the data
    pool = Pool(data)

    # Predict outcomes using the model
    predictions = model.predict(pool)

    return predictions