8000 Fix three typos in manifold documentation by impaktor · Pull Request #9990 · scikit-learn/scikit-learn · GitHub
[go: up one dir, main page]
Skip to content

Fix three typos in manifold documentation #9990

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 1 commit into from
Oct 24, 2017
Merged

Fix three typos in manifold documentation #9990

Changes from all commits
Commits
Show all changes
1 commit
Select commit
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
6 changes: 3 additions & 3 deletions doc/modules/manifold.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -533,7 +533,7 @@ the quality of the resulting embedding:
* maximum number of iterations
* angle (not used in the exact method)

The perplexity is defined as :math:`k=2^(S)` where :math:`S` is the Shannon
The perplexity is defined as :math:`k=2^{(S)}` where :math:`S` is the Shannon
entropy of the conditional probability distribution. The perplexity of a
:math:`k`-sided die is :math:`k`, so that :math:`k` is effectively the number of
nearest neighbors t-SNE considers when generating the conditional probabilities.
Expand Down Expand Up @@ -598,8 +598,8 @@ where label regions largely overlap. This is a strong clue that this data can
be well separated by non linear methods that focus on the local structure (e.g.
an SVM with a Gaussian RBF kernel). However, failing to visualize well
separated homogeneously labeled groups with t-SNE in 2D does not necessarily
implie that the data cannot be correctly classified by a supervised model. It
might be the case that 2 dimensions are not enough low to accurately represents
imply that the data cannot be correctly classified by a supervised model. It
might be the case that 2 dimensions are not low enough to accurately represents
the internal structure of the data.


Expand Down
0