def apply_model(model_object, feature_matrix):
    """Applies trained GBT model to new examples.

    E = number of examples (storm objects)
    Z = number of features (input variables)

    :param model_object: Trained instance of `xgboost.XGBClassifier`.
    :param feature_matrix: E-by-Z numpy array of features.
    :return: class_probability_matrix: E-by-K numpy array of class
        probabilities.  class_probability_matrix[i, k] is the forecast
        probability that the [i]th storm object belongs to the [k]th class.
        Classes are mutually exclusive and collectively exhaustive, so the sum
        across each row is 1.
    """

    return model_object.predict_proba(feature_matrix)