h2o-py/h2o/estimators/estimator_base.py
def start(self, x, y=None, training_frame=None, offset_column=None, fold_column=None,
              weights_column=None, validation_frame=None, **params):
        """
        Train the model asynchronously (to block for results call :meth:`join`).

        :param x: A list of column names or indices indicating the predictor columns.
        :param y: An index or a column name indicating the response column.
        :param H2OFrame training_frame: The H2OFrame having the columns indicated by x and y (as well as any
            additional columns specified by fold, offset, and weights).
        :param offset_column: The name or index of the column in training_frame that holds the offsets.
        :param fold_column: The name or index of the column in training_frame that holds the per-row fold
            assignments.
        :param weights_column: The name or index of the column in training_frame that holds the per-row weights.
        :param validation_frame: H2OFrame with validation data to be scored on while training.
        """
        self._future = True
        self.train(x=x,
                   y=y,
                   training_frame=training_frame,
                   offset_column=offset_column,
                   fold_column=fold_column,
                   weights_column=weights_column,
                   validation_frame=validation_frame,
                   **params)
def start(self, x, y=None, training_frame=None, offset_column=None, fold_column=None,
              weights_column=None, validation_frame=None, **params):
        """
        Train the model asynchronously (to block for results call :meth:`join`).

        :param x: A list of column names or indices indicating the predictor columns.
        :param y: An index or a column name indicating the response column.
        :param H2OFrame training_frame: The H2OFrame having the columns indicated by x and y (as well as any
            additional columns specified by fold, offset, and weights).
        :param offset_column: The name or index of the column in training_frame that holds the offsets.
        :param fold_column: The name or index of the column in training_frame that holds the per-row fold
            assignments.
        :param weights_column: The name or index of the column in training_frame that holds the per-row weights.
        :param validation_frame: H2OFrame with validation data to be scored on while training.
        """
        self._future = True
        self.train(x=x,
                   y=y,
                   training_frame=training_frame,
                   offset_column=offset_column,
                   fold_column=fold_column,
                   weights_column=weights_column,
                   validation_frame=validation_frame,
                   **params)
Train the model asynchronously (to block for results call :meth:`join`).

        :param x: A list of column names or indices indicating the predictor columns.
        :param y: An index or a column name indicating the response column.
        :param H2OFrame training_frame: The H2OFrame having the columns indicated by x and y (as well as any
            additional columns specified by fold, offset, and weights).
        :param offset_column: The name or index of the column in training_frame that holds the offsets.
        :param fold_column: The name or index of the column in training_frame that holds the per-row fold
            assignments.
        :param weights_column: The name or index of the column in training_frame that holds the per-row weights.
        :param validation_frame: H2OFrame with validation data to be scored on while training.