Skip to content

[MRG] DOC Mention StandardScaler ddof #12950

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 7 commits into from
Jan 14, 2019
Merged

[MRG] DOC Mention StandardScaler ddof #12950

Changes from all commits
Commits
Show all changes
7 commits
Select commit Hold shift + click to select a range
60e25fe
Update docstring of preprocessing.StandardScaler so it's clear that t…
Jan 10, 2019
b406ded
Add np.sqrt to calculation of standard deviation
Jan 10, 2019
c91101e
Change np to numpy
Jan 10, 2019
06f807d
Add message explaning it's unlikely that the choice of estimator woul…
Jan 11, 2019
a16334d
Move note about ddof in StandardScaler to 'Notes' section, and use np…
Jan 11, 2019
dc9c0ee
Remove 'however'
Jan 11, 2019
b62c70d
Replicate note in the function
Jan 14, 2019
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
8 changes: 8 additions & 0 deletions sklearn/preprocessing/data.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -123,6 +123,10 @@ def scale(X, axis=0, with_mean=True, with_std=True, copy=True):
NaNs are treated as missing values: disregarded to compute the statistics,
and maintained during the data transformation.

We use a biased estimator for the standard deviation, equivalent to
`numpy.std(x, ddof=0)`. Note that the choice of `ddof` is unlikely to
affect model performance.

For a comparison of the different scalers, transformers, and normalizers,
see :ref:`examples/preprocessing/plot_all_scaling.py
<sphx_glr_auto_examples_preprocessing_plot_all_scaling.py>`.
Expand Down Expand Up @@ -574,6 +578,10 @@ class StandardScaler(BaseEstimator, TransformerMixin):
-----
NaNs are treated as missing values: disregarded in fit, and maintained in
transform.

We use a biased estimator for the standard deviation, equivalent to
`numpy.std(x, ddof=0)`. Note that the choice of `ddof` is unlikely to
affect model performance.

For a comparison of the different scalers, transformers, and normalizers,
see :ref:`examples/preprocessing/plot_all_scaling.py
Expand Down