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Set default n_splits=5 in scikit-learn KFold #143

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Oct 11, 2018
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Set default n_splits=5 in scikit-learn KFold #143

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4 changes: 2 additions & 2 deletions verde/model_selection.py
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Original file line number Diff line number Diff line change
Expand Up @@ -159,7 +159,7 @@ def cross_val_score(estimator, coordinates, data, weights=None, cv=None, client=
>>> # A linear trend should perfectly predict this data
>>> scores = cross_val_score(Trend(degree=1), coords, data)
>>> print(', '.join(['{:.2f}'.format(score) for score in scores]))
1.00, 1.00, 1.00
1.00, 1.00, 1.00, 1.00, 1.00

>>> # To run parallel, we need to create a dask.distributed Client. It will
>>> # create a local cluster if no arguments are given so we can run the
Expand All @@ -182,7 +182,7 @@ def cross_val_score(estimator, coordinates, data, weights=None, cv=None, client=
if client is None:
client = DummyClient()
if cv is None:
cv = KFold(shuffle=True, random_state=0)
cv = KFold(shuffle=True, random_state=0, n_splits=5)
ndata = data[0].size
args = (coordinates, data, weights)
scores = []
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