From 2bb9653b613f46bbaa6f6670794b00ecc3edcc9b Mon Sep 17 00:00:00 2001 From: Suriyan Laohaprapanon Date: Thu, 13 Jun 2024 08:56:04 +0200 Subject: [PATCH] fix syntax error in README that would not be rendered on PyPI --- README.rst | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/README.rst b/README.rst index 6a9a8b6..233e251 100644 --- a/README.rst +++ b/README.rst @@ -21,17 +21,22 @@ Non-Hispanic Blacks, Asians, and Hispanics. New Package With New Models in Pytorch ---------------------------------------- + https://github.com/appeler/ethnicolr2 + Streamlit App ------------ +--------------- + https://ethnicolr.streamlit.app/ + Caveats and Notes ----------------------- If you picked a person at random with the last name 'Smith' in the US in 2010 and asked us to guess this person's race (as measured by the census), the best guess would be based on what is available from the aggregated Census file. It is the Bayes Optimal Solution. So what good are last-name-only predictive models for? A few things---if you want to impute race and ethnicity for last names that are not in the census file, infer the race and ethnicity in different years than when the census was conducted (if some assumptions hold), infer the race of people in different countries (if some assumptions hold), etc. The biggest benefit comes in cases where both the first name and last name are known. + Install ----------