(self, num_trees=30,
                 do_bootstrapping=True,
                 n_points_per_tree=0,
                 compute_oob_error=False,
                 return_total_variance=True,
                 rng=None)
Interface for the random_forest_run library to model the
        objective function with a random forest.

        Parameters
        ----------
        num_trees: int
            The number of trees in the random forest.
        do_bootstrapping: bool
            Turns on / off bootstrapping in the random forest.
        n_points_per_tree: int
            Number of data point per tree. If set to 0 then we will use all data points in each tree
        compute_oob_error: bool
            Turns on / off calculation of out-of-bag error. Default: False
        return_total_variance: bool
            Return law of total variance (mean of variances + variance of means, if True)
            or explained variance (variance of means, if False). Default: True
        rng: np.random.RandomState
            Random number generator
Interface for the random_forest_run library to model the
        objective function with a random forest.
Interface for the random_forest_run library to model the objective function with a random forest .
def __init__(self, num_trees=30,
                 do_bootstrapping=True,
                 n_points_per_tree=0,
                 compute_oob_error=False,
                 return_total_variance=True,
                 rng=None):
        """
        Interface for the random_forest_run library to model the
        objective function with a random forest.

        Parameters
        ----------
        num_trees: int
            The number of trees in the random forest.
        do_bootstrapping: bool
            Turns on / off bootstrapping in the random forest.
        n_points_per_tree: int
            Number of data point per tree. If set to 0 then we will use all data points in each tree
        compute_oob_error: bool
            Turns on / off calculation of out-of-bag error. Default: False
        return_total_variance: bool
            Return law of total variance (mean of variances + variance of means, if True)
            or explained variance (variance of means, if False). Default: True
        rng: np.random.RandomState
            Random number generator
        """

        if rng is None:
            self.rng = np.random.RandomState()
        else:
            self.rng = rng

        self.reg_rng = reg.default_random_engine(self.rng.randint(1000))

        self.n_points_per_tree = n_points_per_tree

        self.rf = reg.binary_rss_forest()
        self.rf.options.num_trees = num_trees
        self.rf.options.do_bootstrapping = do_bootstrapping
        self.rf.options.num_data_points_per_tree = n_points_per_tree
        self.rf.options.compute_oob_error = compute_oob_error
        self.rf.options.compute_law_of_total_variance = return_total_variance
def __init__ ( self , num_trees = 30 , do_bootstrapping = True , n_points_per_tree = 0 , compute_oob_error = False , return_total_variance = True , rng = None ) : if rng is None : self . rng = np . random . RandomState ( ) else : self . rng = rng self . reg_rng = reg . default_random_engine ( self . rng . randint ( 1000 ) ) self . n_points_per_tree = n_points_per_tree self . rf = reg . binary_rss_forest ( ) self . rf . options . num_trees = num_trees self . rf . options . do_bootstrapping = do_bootstrapping self . rf . options . num_data_points_per_tree = n_points_per_tree self . rf . options . compute_oob_error = compute_oob_error self . rf . options . compute_law_of_total_variance = return_total_variance