def crossvalidate_model(self, classifier, data, num_folds, rnd, output=None):
        """
        Crossvalidates the model using the specified data, number of folds and random number generator wrapper.

        :param classifier: the classifier to cross-validate
        :type classifier: Classifier
        :param data: the data to evaluate on
        :type data: Instances
        :param num_folds: the number of folds
        :type num_folds: int
        :param rnd: the random number generator to use
        :type rnd: Random
        :param output: the output generator to use
        :type output: PredictionOutput
        """
        if output is None:
            generator = []
        else:
            generator = [output.jobject]
        javabridge.call(
            self.jobject, "crossValidateModel",
            "(Lweka/classifiers/Classifier;Lweka/core/Instances;ILjava/util/Random;[Ljava/lang/Object;)V",
            classifier.jobject, data.jobject, num_folds, rnd.jobject, generator)
def crossvalidate_model ( self , classifier , data , num_folds , rnd , output = None ) : if output is None : generator = [ ] else : generator = [ output . jobject ] javabridge . call ( self . jobject , "crossValidateModel" , "(Lweka/classifiers/Classifier;Lweka/core/Instances;ILjava/util/Random;[Ljava/lang/Object;)V" , classifier . jobject , data . jobject , num_folds , rnd . jobject , generator )
Crossvalidates the model using the specified data, number of folds and random number generator wrapper.

        :param classifier: the classifier to cross-validate
        :type classifier: Classifier
        :param data: the data to evaluate on
        :type data: Instances
        :param num_folds: the number of folds
        :type num_folds: int
        :param rnd: the random number generator to use
        :type rnd: Random
        :param output: the output generator to use
        :type output: PredictionOutput