DOC Update loss from LAD to absolute_error
@@ -706,13 +706,13 @@ space.
.. note::
- For some losses, e.g. the least absolute deviation (LAD) where the gradients
+ For some losses, e.g. ``'absolute_error'`` where the gradients
are :math:`\pm 1`, the values predicted by a fitted :math:`h_m` are not
accurate enough: the tree can only output integer values. As a result, the
leaves values of the tree :math:`h_m` are modified once the tree is
fitted, such that the leaves values minimize the loss :math:`L_m`. The
- update is loss-dependent: for the LAD loss, the value of a leaf is updated
- to the median of the samples in that leaf.
+ update is loss-dependent: for the absolute error loss, the value of
+ a leaf is updated to the median of the samples in that leaf.
Classification
^^^^^^^^^^^^^^
@@ -749,7 +749,7 @@ the parameter ``loss``:
* Squared error (``'squared_error'``): The natural choice for regression
due to its superior computational properties. The initial model is
given by the mean of the target values.
- * Least absolute deviation (``'lad'``): A robust loss function for
+ * Absolute error (``'absolute_error'``): A robust loss function for
regression. The initial model is given by the median of the
target values.
* Huber (``'huber'``): Another robust loss function that combines
