(self, X, y, **kwargs)
Trains the random forest on X and y.

        Parameters
        ----------
        X: np.ndarray (N, D)
            Input data points. The dimensionality of X is (N, D),
            with N as the number of points and D is the number of features.
        y: np.ndarray (N,)
            The corresponding target values.
Trains the random forest on X and y.
Trains the random forest on X and y .
def train(self, X, y, **kwargs):
        """
        Trains the random forest on X and y.

        Parameters
        ----------
        X: np.ndarray (N, D)
            Input data points. The dimensionality of X is (N, D),
            with N as the number of points and D is the number of features.
        y: np.ndarray (N,)
            The corresponding target values.
        """

        self.X = X
        self.y = y

        if self.n_points_per_tree == 0:
            self.rf.options.num_data_points_per_tree = X.shape[0]

        data = reg.default_data_container(self.X.shape[1])

        for row_X, row_y in zip(X, y):
            data.add_data_point(row_X, row_y)

        self.rf.fit(data, self.reg_rng)
def train ( self , X , y , * * kwargs ) : self . X = X self . y = y if self . n_points_per_tree == 0 : self . rf . options . num_data_points_per_tree = X . shape [ 0 ] data = reg . default_data_container ( self . X . shape [ 1 ] ) for row_X , row_y in zip ( X , y ) : data . add_data_point ( row_X , row_y ) self . rf . fit ( data , self . reg_rng )