readme cleanup

CDonnerer · Jun 20, 2021 · 9ab81ba · 9ab81ba
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diff --git a/README.rst b/README.rst
@@ -73,9 +73,8 @@ the two models is essentially identical, XGBDistribution is **50x faster**
 (timed on both fit and predict steps).
 
 Note that the speed-up will decrease with dataset size, as it is ultimately
-limited by the natural gradient computation (via `LAPACK gesv`_, which scales
-with O(N^3)), with 1m rows of data ``XGBDistribution`` is still 10x faster than
-``NGBRegressor``.
+limited by the natural gradient computation (via `LAPACK gesv`_), with 1m rows
+of data ``XGBDistribution`` is still 10x faster than ``NGBRegressor``.
 
 .. image:: https://raw.githubusercontent.com/CDonnerer/xgboost-distribution/main/imgs/performance_comparison.png
           :align: center

diff --git a/src/xgboost_distribution/distributions/normal.py b/src/xgboost_distribution/distributions/normal.py
@@ -62,7 +62,6 @@ def predict(self, params):
 
         # TODO: this should go elsewhere
         Predictions = namedtuple("Predictions", (p for p in self.params))
-
         return Predictions(loc=loc, scale=scale)
 
     def starting_params(self, y):