Change low_memory default in docstrings
@@ -287,7 +287,7 @@ def nearest_neighbors(
random_state: np.random state
The random state to use for approximate NN computations.
- low_memory: bool (optional, default False)
+ low_memory: bool (optional, default True)
Whether to pursue lower memory NNdescent.
verbose: bool (optional, default False)
@@ -1438,7 +1438,7 @@ class UMAP(BaseEstimator):
The effective scale of embedded points. In combination with ``min_dist``
this determines how clustered/clumped the embedded points are.
- low_memory: bool (optional, default False)
+ low_memory: bool (optional, default True)
For some datasets the nearest neighbor computation can consume a lot of
memory. If you find that UMAP is failing due to memory constraints
consider setting this option to True. This approach is more
