h2o-py/h2o/model/binomial.py
def plot(self, timestep="AUTO", metric="AUTO", server=False, **kwargs):
        """
        Plot training set (and validation set if available) scoring history for an H2OBinomialModel.

        The timestep and metric arguments are restricted to what is available in its scoring history.

        :param str timestep: A unit of measurement for the x-axis.
        :param str metric: A unit of measurement for the y-axis.
        :param bool server: if True, then generate the image inline (using matplotlib's "Agg" backend)
        """
        assert_is_type(metric, "AUTO", "logloss", "auc", "classification_error", "rmse")
        if self._model_json["algo"] in ("deeplearning", "deepwater", "xgboost", "drf", "gbm"):
            if metric == "AUTO":
                metric = "logloss"
        self._plot(timestep=timestep, metric=metric, server=server)
def plot(self, timestep="AUTO", metric="AUTO", server=False, **kwargs):
        """
        Plot training set (and validation set if available) scoring history for an H2OBinomialModel.

        The timestep and metric arguments are restricted to what is available in its scoring history.

        :param str timestep: A unit of measurement for the x-axis.
        :param str metric: A unit of measurement for the y-axis.
        :param bool server: if True, then generate the image inline (using matplotlib's "Agg" backend)
        """
        assert_is_type(metric, "AUTO", "logloss", "auc", "classification_error", "rmse")
        if self._model_json["algo"] in ("deeplearning", "deepwater", "xgboost", "drf", "gbm"):
            if metric == "AUTO":
                metric = "logloss"
        self._plot(timestep=timestep, metric=metric, server=server)
def plot ( self , timestep = "AUTO" , metric = "AUTO" , server = False , * * kwargs ) : assert_is_type ( metric , "AUTO" , "logloss" , "auc" , "classification_error" , "rmse" ) if self . _model_json [ "algo" ] in ( "deeplearning" , "deepwater" , "xgboost" , "drf" , "gbm" ) : if metric == "AUTO" : metric = "logloss" self . _plot ( timestep = timestep , metric = metric , server = server )
Plot training set (and validation set if available) scoring history for an H2OBinomialModel.

        The timestep and metric arguments are restricted to what is available in its scoring history.

        :param str timestep: A unit of measurement for the x-axis.
        :param str metric: A unit of measurement for the y-axis.
        :param bool server: if True, then generate the image inline (using matplotlib's "Agg" backend)
Plot training set ( and validation set if available ) scoring history for an H2OBinomialModel .