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commit 48c193dd1a10c71c2aeaf7730b3740ab04b90745 1 parent 6c7496f
@ogrisel ogrisel authored
Showing with 4 additions and 4 deletions.
  1. +4 −4 sklearn/feature_selection/univariate_selection.py
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8 sklearn/feature_selection/univariate_selection.py
@@ -47,17 +47,17 @@ def f_oneway(*args):
1. The samples are independent
2. Each sample is from a normally distributed population
- 3. The population standard deviations of the groups are all equal. This
+ 3. The population standard deviations of the groups are all equal. This
property is known as homocedasticity.
If these assumptions are not true for a given set of data, it may still be
possible to use the Kruskal-Wallis H-test (`stats.kruskal`_) although with
- some loss of power
+ some loss of power.
The algorithm is from Heiman[2], pp.394-7.
- See scipy.stats.f_oneway that should give the same results while
- being less efficient
+ See ``scipy.stats.f_oneway`` that should give the same results while
+ being less efficient.
Notes
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