def staged_predict_proba(self, test_data):
        """
        Predict class probabilities at each stage of an H2O Model (only GBM models).

        The output structure is analogous to the output of function predict_leaf_node_assignment. For each tree t and
        class c there will be a column Tt.Cc (eg. T3.C1 for tree 3 and class 1). The value will be the corresponding
        predicted probability of this class by combining the raw contributions of trees T1.Cc,..,TtCc. Binomial models
        build the trees just for the first class and values in columns Tx.C1 thus correspond to the the probability p0.

        :param H2OFrame test_data: Data on which to make predictions.

        :returns: A new H2OFrame of staged predictions.
        """
        if not isinstance(test_data, h2o.H2OFrame): raise ValueError("test_data must be an instance of H2OFrame")
        j = h2o.api("POST /3/Predictions/models/%s/frames/%s" % (self.model_id, test_data.frame_id),
                    data={"predict_staged_proba": True})
        return h2o.get_frame(j["predictions_frame"]["name"])
def staged_predict_proba(self, test_data):
        """
        Predict class probabilities at each stage of an H2O Model (only GBM models).

        The output structure is analogous to the output of function predict_leaf_node_assignment. For each tree t and
        class c there will be a column Tt.Cc (eg. T3.C1 for tree 3 and class 1). The value will be the corresponding
        predicted probability of this class by combining the raw contributions of trees T1.Cc,..,TtCc. Binomial models
        build the trees just for the first class and values in columns Tx.C1 thus correspond to the the probability p0.

        :param H2OFrame test_data: Data on which to make predictions.

        :returns: A new H2OFrame of staged predictions.
        """
        if not isinstance(test_data, h2o.H2OFrame): raise ValueError("test_data must be an instance of H2OFrame")
        j = h2o.api("POST /3/Predictions/models/%s/frames/%s" % (self.model_id, test_data.frame_id),
                    data={"predict_staged_proba": True})
        return h2o.get_frame(j["predictions_frame"]["name"])
def staged_predict_proba ( self , test_data ) : if not isinstance ( test_data , h2o . H2OFrame ) : raise ValueError ( "test_data must be an instance of H2OFrame" ) j = h2o . api ( "POST /3/Predictions/models/%s/frames/%s" % ( self . model_id , test_data . frame_id ) , data = { "predict_staged_proba" : True } ) return h2o . get_frame ( j [ "predictions_frame" ] [ "name" ] )
Predict class probabilities at each stage of an H2O Model (only GBM models).

        The output structure is analogous to the output of function predict_leaf_node_assignment. For each tree t and
        class c there will be a column Tt.Cc (eg. T3.C1 for tree 3 and class 1). The value will be the corresponding
        predicted probability of this class by combining the raw contributions of trees T1.Cc,..,TtCc. Binomial models
        build the trees just for the first class and values in columns Tx.C1 thus correspond to the the probability p0.

        :param H2OFrame test_data: Data on which to make predictions.

        :returns: A new H2OFrame of staged predictions.
Predict class probabilities at each stage of an H2O Model ( only GBM models ) .