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DOC fixed default values in dbscan #15753

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Merged
merged 2 commits into from
Dec 2, 2019
Merged

DOC fixed default values in dbscan #15753

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75548b7
fixed default values in dbscan
Dec 2, 2019
7dde012
fixed metric default value for dbscan
Dec 2, 2019
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16 changes: 8 additions & 8 deletions sklearn/cluster/_dbscan.py
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Expand Up @@ -156,18 +156,18 @@ class DBSCAN(ClusterMixin, BaseEstimator):

Parameters
----------
eps : float, optional
eps : float, default=0.5
The maximum distance between two samples for one to be considered
as in the neighborhood of the other. This is not a maximum bound
on the distances of points within a cluster. This is the most
important DBSCAN parameter to choose appropriately for your data set
and distance function.

min_samples : int, optional
min_samples : int, default=5
The number of samples (or total weight) in a neighborhood for a point
to be considered as a core point. This includes the point itself.

metric : string, or callable
metric : string, or callable, default='euclidean'
The metric to use when calculating distance between instances in a
feature array. If metric is a string or callable, it must be one of
the options allowed by :func:`sklearn.metrics.pairwise_distances` for
Expand All @@ -179,27 +179,27 @@ class DBSCAN(ClusterMixin, BaseEstimator):
.. versionadded:: 0.17
metric *precomputed* to accept precomputed sparse matrix.

metric_params : dict, optional
metric_params : dict, default=None
Additional keyword arguments for the metric function.

.. versionadded:: 0.19

algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional
algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'
The algorithm to be used by the NearestNeighbors module
to compute pointwise distances and find nearest neighbors.
See NearestNeighbors module documentation for details.

leaf_size : int, optional (default = 30)
leaf_size : int, default=30
Leaf size passed to BallTree or cKDTree. This can affect the speed
of the construction and query, as well as the memory required
to store the tree. The optimal value depends
on the nature of the problem.

p : float, optional
p : float, default=None
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This is more a comment to the reviewers than the contributor: I find the change to default=None for a parameter like this extremely unhelpful. What does "None" mean in the context of "the power of the minkowski metric"? When the user chooses metric='minkowski', how is this None interpreted?

The power of the Minkowski metric to be used to calculate distance
between points.

n_jobs : int or None, optional (default=None)
n_jobs : int or None, default=None
The number of parallel jobs to run.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
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