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authorYuchen Pei <me@ypei.me>2019-01-04 15:14:11 +0100
committerYuchen Pei <me@ypei.me>2019-01-04 15:14:11 +0100
commitcc8c8d0fbe7caa0800c9a8749dfa4c65b4bccc56 (patch)
tree7f8048d1602ac8925b10f2d704e08c9b78c9b3c5 /posts
parent312842f6d75cf6b2f04d0308fec91ff908720f65 (diff)
minor
Diffstat (limited to 'posts')
-rw-r--r--posts/2019-01-03-discriminant-analysis.md3
1 files changed, 1 insertions, 2 deletions
diff --git a/posts/2019-01-03-discriminant-analysis.md b/posts/2019-01-03-discriminant-analysis.md
index d2aaaa3..28c53d5 100644
--- a/posts/2019-01-03-discriminant-analysis.md
+++ b/posts/2019-01-03-discriminant-analysis.md
@@ -176,8 +176,7 @@ will result in a lossy compression / regularisation equivalent to doing
analysis](https://en.wikipedia.org/wiki/Principal_component_analysis) on
$(M - \bar x) V_x D_x^{-1}$.
-Note that as of 2019-01-04, in the scikit-learn implementation, the prediction of
-LDA is done without
+Note that as of 2019-01-04, in the [scikit-learn implementation of LDA](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/discriminant_analysis.py), the prediction is done without
any lossy compression, even if the parameter `n_components` is set to be smaller
than dimension of the affine space spanned by the centroids.
In other words, the prediction does not change regardless of `n_components`.