8000 DOC Ensures that PLSCanonical passes numpydoc validation by Pinky-Chaudhary · Pull Request #21175 · scikit-learn/scikit-learn · GitHub
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DOC Ensures that PLSCanonical passes numpydoc validation #21175

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Sep 28, 2021
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DOC Ensures that PLSCanonical passes numpydoc validation #21175

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1 change: 0 additions & 1 deletion maint_tools/test_docstrings.py
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Expand Up @@ -23,7 +23,6 @@
"MultiTaskLassoCV",
"OrthogonalMatchingPursuit",
"OrthogonalMatchingPursuitCV",
"PLSCanonical",
"PLSRegression",
"PLSSVD",
"PassiveAggressiveClassifier",
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12 changes: 6 additions & 6 deletions sklearn/cross_decomposition/_pls.py
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Expand Up @@ -680,7 +680,7 @@ class PLSCanonical(_PLS):
will compute the whole SVD.

max_iter : int, default=500
the maximum number of iterations of the power method when
The maximum number of iterations of the power method when
`algorithm='nipals'`. Ignored otherwise.

tol : float, default=1e-06
Expand Down Expand Up @@ -748,6 +748,11 @@ class PLSCanonical(_PLS):

.. versionadded:: 1.0

See Also
--------
CCA : Canonical Correlation Analysis.
PLSSVD : Partial Least Square SVD.

Examples
--------
>>> from sklearn.cross_decomposition import PLSCanonical
Expand All @@ -757,11 +762,6 @@ class PLSCanonical(_PLS):
>>> plsca.fit(X, Y)
PLSCanonical()
>>> X_c, Y_c = plsca.transform(X, Y)

See Also
--------
CCA
PLSSVD
"""

# This implementation provides the same results that the "plspm" package
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0