reword docstrings

imblearn/under_sampling/_prototype_selection/_nearmiss.py

@@ -35,28 +35,25 @@ class NearMiss(BaseUnderSampler):
 
  n_neighbors : int or estimator object, default=3
  If ``int``, size of the neighbourhood to consider to compute the
- average distance to the minority point samples. If object, an
+ average distance to the minority samples. If object, an
  estimator that inherits from
  :class:`~sklearn.neighbors.base.KNeighborsMixin` that will be used to
  find the k_neighbors.
- By default, it will be a 3-NN.
 
  n_neighbors_ver3 : int or estimator object, default=3
- If ``int``, NearMiss-3 algorithm start by a phase of re-sampling. This
- parameter correspond to the number of neighbours selected create the
- subset in which the selection will be performed. If object, an
- estimator that inherits from
+ Only used if `version=3`. If ``int``, the number of target class samples that
+ are closest to a minority sample that will be retained in the first subsampling
+ step. If object, an estimator that inherits from
  :class:`~sklearn.neighbors.base.KNeighborsMixin` that will be used to
  find the k_neighbors.
- By default, it will be a 3-NN.
 
  {n_jobs}
 
  Attributes
  ----------
  sampling_strategy_ : dict
  Dictionary containing the information to sample the dataset. The keys
- corresponds to the class labels from which to sample and the values
+ correspond to the class labels from which to sample and the values
  are the number of samples to sample.
 
  nn_ : estimator object
@@ -144,7 +141,7 @@ def __init__(
  def _selection_dist_based(
  self, X, y, dist_vec, num_samples, key, sel_strategy="nearest"
  ):
- """Select the appropriate samples depending of the strategy selected.
+ """Select the appropriate samples depending on the selected strategy.
 
  Parameters
  ----------