8000 DOC Clarify sorting order of PCA components (#24531) · scikit-learn/scikit-learn@44860bd · GitHub
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DOC Clarify sorting order of PCA components (#24531)
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‎sklearn/decomposition/_incremental_pca.py‎

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@@ -73,7 +73,7 @@ class IncrementalPCA(_BasePCA):
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Principal axes in feature space, representing the directions of
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maximum variance in the data. Equivalently, the right singular
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vectors of the centered input data, parallel to its eigenvectors.
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The components are sorted by ``explained_variance_``.
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The components are sorted by decreasing ``explained_variance_``.
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explained_variance_ : ndarray of shape (n_components,)
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Variance explained by each of the selected components.

‎sklearn/decomposition/_pca.py‎

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@@ -233,7 +233,7 @@ class PCA(_BasePCA):
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Principal axes in feature space, representing the directions of
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maximum variance in the data. Equivalently, the right singular
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vectors of the centered input data, parallel to its eigenvectors.
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The components are sorted by ``explained_variance_``.
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The components are sorted by decreasing ``explained_variance_``.
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explained_variance_ : ndarray of shape (n_components,)
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The amount of variance explained by each of the selected components.

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