h2o-py/h2o/automl/autoh2o.py
def predict(self, test_data):
        """
        Predict on a dataset.

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

        :returns: A new H2OFrame of predictions.

        :examples:
        >>> # Set up an H2OAutoML object
        >>> aml = H2OAutoML(max_runtime_secs=30)
        >>> # Launch an H2OAutoML run
        >>> aml.train(y=y, training_frame=train)
        >>> # Predict with top model from AutoML Leaderboard on a H2OFrame called 'test'
        >>> aml.predict(test)

        """
        if self._fetch():
            self._model = h2o.get_model(self._leader_id)
            return self._model.predict(test_data)
        print("No model built yet...")
def predict(self, test_data):
        """
        Predict on a dataset.

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

        :returns: A new H2OFrame of predictions.

        :examples:
        >>> # Set up an H2OAutoML object
        >>> aml = H2OAutoML(max_runtime_secs=30)
        >>> # Launch an H2OAutoML run
        >>> aml.train(y=y, training_frame=train)
        >>> # Predict with top model from AutoML Leaderboard on a H2OFrame called 'test'
        >>> aml.predict(test)

        """
        if self._fetch():
            self._model = h2o.get_model(self._leader_id)
            return self._model.predict(test_data)
        print("No model built yet...")
Predict on a dataset.

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

        :returns: A new H2OFrame of predictions.

        :examples:
        >>> # Set up an H2OAutoML object
        >>> aml = H2OAutoML(max_runtime_secs=30)
        >>> # Launch an H2OAutoML run
        >>> aml.train(y=y, training_frame=train)
        >>> # Predict with top model from AutoML Leaderboard on a H2OFrame called 'test'
        >>> aml.predict(test)