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[MRG+2] addresses #8509 improvements to f_regression documentation #8548

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Merged
merged 7 commits into from
Mar 7, 2017
Merged

[MRG+2] addresses #8509 improvements to f_regression documentation #8548

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b69abe3
clarify role of the function and streamline introduction
Mar 6, 2017
4acd11d
added feature selection methods to see also
Mar 6, 2017
759a10c
completed see also
Mar 6, 2017
92ea942
fixed pep related formatting for flake8checks.
Mar 7, 2017
7fb0427
fixed extra whitespace flake8 problems, remaining failure is a copied…
Mar 7, 2017
2f13f2a
one more whitespace
Mar 7, 2017
d8f438b
FIX small pep8 error.
NelleV Mar 7, 2017
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17 changes: 13 additions & 4 deletions sklearn/feature_selection/univariate_selection.py
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Expand Up @@ -230,17 +230,18 @@ def chi2(X, y):
def f_regression(X, y, center=True):
"""Univariate linear regression tests.

Quick linear model for testing the effect of a single regressor,
sequentially for many regressors.
Linear model for testing the individual effect of each of many regressors.
This is a scoring function to be used in a feature seletion procedure, not
a free standing feature selection procedure.

This is done in 2 steps:

1. The cross correlation between each regressor and the target is computed,
1. The correlation between each regressor and the target is computed,
that is, ((X[:, i] - mean(X[:, i])) * (y - mean_y)) / (std(X[:, i]) *
std(y)).
2. It is converted to an F score then to a p-value.

Read more in the :ref:`User Guide <univariate_feature_selection>`.
For more on usage see the :ref:`User Guide <univariate_feature_selection>`.
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Not sure about this change. Nearly every class/function docstring includes "Read more in the".

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I changed it to make it more clear what the user guide provides in relation to the content in the docstring. "Read more" left me to expect more information about f regression, but it actually is a page on feature selection. I didn't notice that the specific "read more in" was used everywhere, consistency might be worth keeping.


Parameters
----------
Expand All @@ -261,10 +262,18 @@ def f_regression(X, y, center=True):
pval : array, shape=(n_features,)
p-values of F-scores.


See also
--------
mutual_info_regression: Mutual information for a continuous target.
f_classif: ANOVA F-value between label/feature for classification tasks.
chi2: Chi-squared stats of non-negative features for classification tasks.
SelectKBest: Select features based on the k highest scores.
SelectFpr: Select features based on a false positive rate test.
SelectFdr: Select features based on an estimated false discovery rate.
SelectFwe: Select features based on family-wise error rate.
SelectPercentile: Select features based on percentile of the highest
scores.
"""
X, y = check_X_y(X, y, ['csr', 'csc', 'coo'], dtype=np.float64)
n_samples = X.shape[0]
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