'Build a decision tree regressor from the training set (X, y).
Parameters
X : array-like or sparse matrix, shape = [n_samples, n_features]
The training input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csc_matrix``.
y : array-like, shape = [n_samples] or [n_samples, n_outputs]
The target values (real numbers). Use ``dtype=np.float64`` and
``order=\'C\'`` for maximum efficiency.
sample_weight : array-like, shape = [n_samples] or None
Sample weights. If None, then samples are equally weighted. Splits
that would create child nodes with net zero or negative weight are
ignored while searching for a split in each node.
check_input : boolean, (default=True)
Allow to bypass several input checking.
Don\'t use this parameter unless you know what you do.
X_idx_sorted : array-like, shape = [n_samples, n_features], optional
The indexes of the sorted training input samples. If many tree
are grown on the same dataset, this allows the ordering to be
cached between trees. If None, the data will be sorted here.
Don\'t use this parameter unless you know what to do.
Returns
self : object
Returns self.'

def fit(self, X, y, sample_weight=None, check_input=True, X_idx_sorted=None):

    super(DecisionTreeRegressor, self).fit(X, y, sample_weight=sample_weight, check_input=check_input, X_idx_sorted=X_idx_sorted)
    return self
