Predict Race/Ethnicity Based on Sequence of Characters in the Name
Project description
ethnicolr: Predict Race and Ethnicity From Name
We exploit the US census data, the Florida voting registration data, and the Wikipedia data collected by Skiena and colleagues, to predict race and ethnicity based on first and last name or just the last name. The granularity at which we predict the race depends on the dataset. For instance, Skiena et al.’ Wikipedia data is at the ethnic group level, while the census data we use in the model (the raw data has additional categories of Native Americans and Bi-racial) merely categorizes between Non-Hispanic Whites, Non-Hispanic Blacks, Asians, and Hispanics.
DIME Race
Data on race of all the people in the DIME data is posted here The underlying python scripts are posted here
Caveats and Notes
If you picked a random individual with last name ‘Smith’ from the US in 2010 and asked us to guess this person’s race (measured as crudely as 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 ethnicity at a more granular level, guess the race of people in different years (than when the census was conducted if some assumptions hold), guess the race of people in different countries (again if some assumptions hold), when names are slightly different (again with some assumptions), etc. The big benefit comes from when both the first name and last name is known.
Install
We strongly recommend installing ethnicolor inside a Python virtual environment (see venv documentation)
pip install ethnicolr
Or
conda install -c soodoku ethnicolr
- Notes:
The models are run and verified on TensorFlow 2.x using Python 3.7 and 3.8 and lower will work. TensorFlow 1.x has been deprecated.
If you are installing on Windows, Theano installation typically needs admin. privileges on the shell.
General API
To see the available command line options for any function, please type in <function-name> --help
# census_ln --help
usage: census_ln [-h] [-y {2000,2010}] [-o OUTPUT] -l LAST input
Appends Census columns by last name
positional arguments:
input Input file
optional arguments:
-h, --help show this help message and exit
-y {2000,2010}, --year {2000,2010}
Year of Census data (default=2000)
-o OUTPUT, --output OUTPUT
Output file with Census data columns
-l LAST, --last LAST Name or index location of column contains the last
name
Examples
To append census data from 2010 to a file without column headers and the first column carries the last name, use -l 0
census_ln -y 2010 -o output-census2010.csv -l 0 input-without-header.csv
To append census data from 2010 to a file with column header in the first row, specify the column name carrying last names using the -l option, keeping the rest the same:
census_ln -y 2010 -o output-census2010.csv -l last_name input-with-header.csv
To predict race/ethnicity using Wikipedia full name model, if the input file doesn’t have any column headers, you must using -l and -f to specify the index of column carrying the last name and first name respectively (first column has index 0).
pred_wiki_name -o output-wiki-pred-race.csv -l 0 -f 1 input-without-header.csv
And to predict race/ethnicity using Wikipedia full name model for a file with column headers, you can specify the column name of last name and first name by using -l and -f flags respectively.
pred_wiki_name -o output-wiki-pred-race.csv -l last_name -f first_name input-with-header.csv
Functions
We expose 6 functions, each of which either take a pandas DataFrame or a CSV. If the CSV doesn’t have a header, we make some assumptions about where the data is:
census_ln(df, namecol, year=2000)
What it does:
Removes extra space
For names in the census file, it appends relevant data of what probability the name provided is of a certain race/ethnicity
Parameters
df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred
namecol : {string, list, int} string or list of the name or location of the column containing the last name
Year : {2000, 2010}, default=2000 year of census to use
Output: Appends the following columns to the pandas DataFrame or CSV: pctwhite, pctblack, pctapi, pctaian, pct2prace, pcthispanic. See here for what the column names mean.
>>> import pandas as pd >>> from ethnicolr import census_ln, pred_census_ln >>> names = [{'name': 'smith'}, ... {'name': 'zhang'}, ... {'name': 'jackson'}] >>> df = pd.DataFrame(names) >>> df name 0 smith 1 zhang 2 jackson >>> census_ln(df, 'name') name pctwhite pctblack pctapi pctaian pct2prace pcthispanic 0 smith 73.35 22.22 0.40 0.85 1.63 1.56 1 zhang 0.61 0.09 98.16 0.02 0.96 0.16 2 jackson 41.93 53.02 0.31 1.04 2.18 1.53pred_census_ln(df, namecol, year=2000, num_iter=100, conf_int=0.9)
What it does:
Removes extra space.
Uses the last name census 2000 model or last name census 2010 model to predict the race and ethnicity.
Parameters
df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred
namecol : {string, list, int} string or list of the name or location of the column containing the last name
year : {2000, 2010}, default=2000 year of census to use
num_iter : int, default=100 number of iterations to calculate uncertainty in model
conf_int : float, default=0.9 confidence interval in predicted class
Output: Appends the following columns to the pandas DataFrame or CSV: race (white, black, asian, or hispanic), api (percentage chance asian), black, hispanic, white. For each race it will provide the mean, standard error, lower & upper bound of confidence interval
(Using the same dataframe from example above)
>>> census_ln(df, 'name') name pctwhite pctblack pctapi pctaian pct2prace pcthispanic 0 smith 73.35 22.22 0.40 0.85 1.63 1.56 1 zhang 0.61 0.09 98.16 0.02 0.96 0.16 2 jackson 41.93 53.02 0.31 1.04 2.18 1.53 >>> census_ln(df, 'name', 2010) name race pctwhite pctblack pctapi pctaian pct2prace pcthispanic 0 smith white 70.9 23.11 0.5 0.89 2.19 2.4 1 zhang api 0.99 0.16 98.06 0.02 0.62 0.15 2 jackson black 39.89 53.04 0.39 1.06 3.12 2.5 >>> pred_census_ln(df, 'name') name race api black hispanic white 0 smith white 0.002019 0.247235 0.014485 0.736260 1 zhang api 0.997807 0.000149 0.000470 0.001574 2 jackson black 0.002797 0.528193 0.014605 0.454405pred_wiki_ln( df, namecol, num_iter=100, conf_int=0.9)
What it does:
Removes extra space.
Uses the last name wiki model to predict the race and ethnicity.
Parameters
df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred
namecol : {string, list, int} string or list of the name or location of the column containing the last name
num_iter : int, default=100 number of iterations to calculate uncertainty in model
conf_int : float, default=0.9 confidence interval in predicted class
Output: Appends the following columns to the pandas DataFrame or CSV: race (categorical variable — category with the highest probability), “Asian,GreaterEastAsian,EastAsian”, “Asian,GreaterEastAsian,Japanese”, “Asian,IndianSubContinent”, “GreaterAfrican,Africans”, “GreaterAfrican,Muslim”, “GreaterEuropean,British”,”GreaterEuropean,EastEuropean”, “GreaterEuropean,Jewish”,”GreaterEuropean,WestEuropean,French”, “GreaterEuropean,WestEuropean,Germanic”,”GreaterEuropean,WestEuropean,Hispanic”, “GreaterEuropean,WestEuropean,Italian”,”GreaterEuropean,WestEuropean,Nordic”. For each race it will provide the mean, standard error, lower & upper bound of confidence interval
>>> import pandas as pd >>> names = [ ... {"last": "smith", "first": "john", "true_race": "GreaterEuropean,British"}, ... { ... "last": "zhang", ... "first": "simon", ... "true_race": "Asian,GreaterEastAsian,EastAsian", ... }, ... ] >>> df = pd.DataFrame(names) >>> from ethnicolr import pred_wiki_ln, pred_wiki_name >>> odf = pred_wiki_ln(df,'last') ['Asian,GreaterEastAsian,EastAsian', 'Asian,GreaterEastAsian,Japanese', 'Asian,IndianSubContinent', 'GreaterAfrican,Africans', 'GreaterAfrican,Muslim', 'GreaterEuropean,British', 'GreaterEuropean,EastEuropean', 'GreaterEuropean,Jewish', 'GreaterEuropean,WestEuropean,French', 'GreaterEuropean,WestEuropean,Germanic', 'GreaterEuropean,WestEuropean,Hispanic', 'GreaterEuropean,WestEuropean,Italian', 'GreaterEuropean,WestEuropean,Nordic'] >>> odf last first ... GreaterEuropean,WestEuropean,Nordic_ub race 0 Smith john ... 0.004559 GreaterEuropean,British 1 Zhang simon ... 0.004076 Asian,GreaterEastAsian,EastAsian [2 rows x 57 columns] >>> odf.iloc[0,:8] last Smith first john true_race GreaterEuropean,British rowindex 0 Asian,GreaterEastAsian,EastAsian_mean 0.004554 Asian,GreaterEastAsian,EastAsian_std 0.003358 Asian,GreaterEastAsian,EastAsian_lb 0.000535 Asian,GreaterEastAsian,EastAsian_ub 0.000705 Name: 0, dtype: objectpred_wiki_name(df, namecol, num_iter=100, conf_int=0.9)
What it does:
Removes extra space.
Uses the full name wiki model to predict the race and ethnicity.
Parameters
df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred
namecol : {string, list} string or list of the name or location of the column containing the first name, last name, middle name, and suffix, if there. The first name and last name columns are required. If no middle name of suffix columns are there, it is assumed that there are no middle names or suffixes.
num_iter : int, default=100 number of iterations to calculate uncertainty in model
conf_int : float, default=0.9 confidence interval in predicted class
Output: Appends the following columns to the pandas DataFrame or CSV: race (categorical variable—category with the highest probability), “Asian,GreaterEastAsian,EastAsian”, “Asian,GreaterEastAsian,Japanese”, “Asian,IndianSubContinent”, “GreaterAfrican,Africans”, “GreaterAfrican,Muslim”, “GreaterEuropean,British”,”GreaterEuropean,EastEuropean”, “GreaterEuropean,Jewish”,”GreaterEuropean,WestEuropean,French”, “GreaterEuropean,WestEuropean,Germanic”,”GreaterEuropean,WestEuropean,Hispanic”, “GreaterEuropean,WestEuropean,Italian”,”GreaterEuropean,WestEuropean,Nordic”. For each race it will provide the mean, standard error, lower & upper bound of confidence interval
(Using the same dataframe from example above)
>>> odf = pred_wiki_name(df, 'last', 'first') ['Asian,GreaterEastAsian,EastAsian', 'Asian,GreaterEastAsian,Japanese', 'Asian,IndianSubContinent', 'GreaterAfrican,Africans', 'GreaterAfrican,Muslim', 'GreaterEuropean,British', 'GreaterEuropean,EastEuropean', 'GreaterEuropean,Jewish', 'GreaterEuropean,WestEuropean,French', 'GreaterEuropean,WestEuropean,Germanic', 'GreaterEuropean,WestEuropean,Hispanic', 'GreaterEuropean,WestEuropean,Italian', 'GreaterEuropean,WestEuropean,Nordic'] >>> odf last first ... GreaterEuropean,WestEuropean,Nordic_ub race 0 Smith john ... 0.000236 GreaterEuropean,British 1 Zhang simon ... 0.000021 Asian,GreaterEastAsian,EastAsian [2 rows x 58 columns] >>> odf.iloc[1,:8] last Zhang first simon true_race Asian,GreaterEastAsian,EastAsian rowindex 1 __name Zhang Simon Asian,GreaterEastAsian,EastAsian_mean 0.890619 Asian,GreaterEastAsian,EastAsian_std 0.119097 Asian,GreaterEastAsian,EastAsian_lb 0.391496 Name: 1, dtype: object
pred_fl_reg_ln(df, namecol, num_iter=100, conf_int=0.9)
What it does?:
Removes extra space, if there.
Uses the last name FL registration model to predict the race and ethnicity.
Parameters
df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred
namecol : {string, list, int} string or list of the name or location of the column containing the last name
num_iter : int, default=100 number of iterations to calculate uncertainty in model
conf_int : float, default=0.9 confidence interval in predicted class
Output: Appends the following columns to the pandas DataFrame or CSV: race (white, black, asian, or hispanic), asian (percentage chance Asian), hispanic, nh_black, nh_white. For each race it will provide the mean, standard error, lower & upper bound of confidence interval
>>> import pandas as pd >>> names = [ ... {"last": "sawyer", "first": "john", "true_race": "nh_white"}, ... {"last": "torres", "first": "raul", "true_race": "hispanic"}, ... ] >>> df = pd.DataFrame(names) >>> from ethnicolr import pred_fl_reg_ln, pred_fl_reg_name, pred_fl_reg_ln_five_cat, pred_fl_reg_name_five_cat >>> odf = pred_fl_reg_ln(df, 'last') ['asian', 'hispanic', 'nh_black', 'nh_white'] >>> odf last first true_race rowindex asian_mean asian_std ... nh_black_ub nh_white_mean nh_white_std nh_white_lb nh_white_ub race 0 Sawyer john nh_white 0 0.004004 0.004483 ... 0.015442 0.908452 0.035121 0.722879 0.804443 nh_white 1 Torres raul hispanic 1 0.005882 0.002249 ... 0.005305 0.182575 0.072142 0.074511 0.090856 hispanic [2 rows x 21 columns] >>> odf.iloc[0] last Sawyer first john true_race nh_white rowindex 0 asian_mean 0.004004 asian_std 0.004483 asian_lb 0.000899 asian_ub 0.00103 hispanic_mean 0.034227 hispanic_std 0.01294 hispanic_lb 0.017406 hispanic_ub 0.017625 nh_black_mean 0.053317 nh_black_std 0.028634 nh_black_lb 0.010537 nh_black_ub 0.015442 nh_white_mean 0.908452 nh_white_std 0.035121 nh_white_lb 0.722879 nh_white_ub 0.804443 race nh_white Name: 0, dtype: objectpred_fl_reg_name(df, namecol, num_iter=100, conf_int=0.9)
What it does:
Removes extra space.
Uses the full name FL model to predict the race and ethnicity.
Parameters
df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred
namecol : {string, list} string or list of the name or location of the column containing the first name, last name, middle name, and suffix, if there. The first name and last name columns are required. If no middle name of suffix columns are there, it is assumed that there are no middle names or suffixes.
num_iter : int, default=100 number of iterations to calculate uncertainty in model
conf_int : float, default=0.9 confidence interval in predicted class
Output: Appends the following columns to the pandas DataFrame or CSV: race (white, black, asian, or hispanic), asian (percentage chance Asian), hispanic, nh_black, nh_white. For each race it will provide the mean, standard error, lower & upper bound of confidence interval
(Using the same dataframe from example above)
>>> odf = pred_fl_reg_name(df, 'last', 'first') ['asian', 'hispanic', 'nh_black', 'nh_white'] >>> odf last first true_race rowindex __name asian_mean ... nh_black_ub nh_white_mean nh_white_std nh_white_lb nh_white_ub race 0 Sawyer john nh_white 0 Sawyer John 0.001196 ... 0.005450 0.971152 0.015757 0.915592 0.918630 nh_white 1 Torres raul hispanic 1 Torres Raul 0.004770 ... 0.000885 0.066303 0.028486 0.022593 0.024143 hispanic [2 rows x 22 columns] >>> odf.iloc[1] last Torres first raul true_race hispanic rowindex 1 __name Torres Raul asian_mean 0.00477 asian_std 0.002943 asian_lb 0.000904 asian_ub 0.001056 hispanic_mean 0.9251 hispanic_std 0.032224 hispanic_lb 0.829494 hispanic_ub 0.8385 nh_black_mean 0.003826 nh_black_std 0.002735 nh_black_lb 0.000838 nh_black_ub 0.000885 nh_white_mean 0.066303 nh_white_std 0.028486 nh_white_lb 0.022593 nh_white_ub 0.024143 race hispanic Name: 1, dtype: object
pred_fl_reg_ln_five_cat(df, namecol, num_iter=100, conf_int=0.9)
What it does?:
Removes extra space, if there.
Uses the last name FL registration model to predict the race and ethnicity.
Parameters
df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred
namecol : {string, list, int} string or list of the name or location of the column containing the last name
num_iter : int, default=100 number of iterations to calculate uncertainty in model
conf_int : float, default=0.9 confidence interval in predicted class
Output: Appends the following columns to the pandas DataFrame or CSV: race (white, black, asian, hispanic or other), asian (percentage chance Asian), hispanic, nh_black, nh_white, other. For each race it will provide the mean, standard error, lower & upper bound of confidence interval
(Using the same dataframe from example above)
>>> odf = pred_fl_reg_ln_five_cat(df,'last') ['asian', 'hispanic', 'nh_black', 'nh_white', 'other'] >>> odf last first true_race rowindex __name asian_mean asian_std ... nh_white_lb nh_white_ub other_mean other_std other_lb other_ub race 0 Sawyer john nh_white 0 Sawyer John 0.142867 0.046145 ... 0.203204 0.221313 0.235889 0.023794 0.192840 0.193671 nh_white 1 Torres raul hispanic 1 Torres Raul 0.101397 0.028399 ... 0.090068 0.100212 0.238645 0.034070 0.136617 0.145928 hispanic [2 rows x 26 columns] >>> odf.iloc[0] last Sawyer first john true_race nh_white rowindex 0 __name Sawyer John asian_mean 0.142867 asian_std 0.046145 asian_lb 0.067382 asian_ub 0.073285 hispanic_mean 0.068199 hispanic_std 0.020641 hispanic_lb 0.02565 hispanic_ub 0.030017 nh_black_mean 0.239793 nh_black_std 0.076287 nh_black_lb 0.084239 nh_black_ub 0.085626 nh_white_mean 0.313252 nh_white_std 0.046173 nh_white_lb 0.203204 nh_white_ub 0.221313 other_mean 0.235889 other_std 0.023794 other_lb 0.19284 other_ub 0.193671 race nh_white Name: 0, dtype: object
pred_fl_reg_name_five_cat(df, namecol, num_iter=100, conf_int=0.9)
What it does:
Removes extra space.
Uses the full name FL model to predict the race and ethnicity.
Parameters
df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred
namecol : {string, list} string or list of the name or location of the column containing the first name, last name, middle name, and suffix, if there. The first name and last name columns are required. If no middle name of suffix columns are there, it is assumed that there are no middle names or suffixes.
num_iter : int, default=100 number of iterations to calculate uncertainty in model
conf_int : float, default=0.9 confidence interval in predicted class
Output: Appends the following columns to the pandas DataFrame or CSV: race (white, black, asian, hispanic, or other), asian (percentage chance Asian), hispanic, nh_black, nh_white, other. For each race it will provide the mean, standard error, lower & upper bound of confidence interval
(Using the same dataframe from example above)
>>> odf = pred_fl_reg_name_five_cat(df, 'last','first') ['asian', 'hispanic', 'nh_black', 'nh_white', 'other'] >>> odf last first true_race rowindex __name asian_mean asian_std ... nh_white_lb nh_white_ub other_mean other_std other_lb other_ub race 0 Sawyer john nh_white 0 Sawyer John 0.194250 0.120314 ... 0.126987 0.167742 0.259069 0.030386 0.142455 0.177375 nh_white 1 Torres raul hispanic 1 Torres Raul 0.081465 0.038318 ... 0.019312 0.020782 0.158614 0.039180 0.081994 0.083105 hispanic [2 rows x 26 columns] >>> odf.iloc[1] last Torres first raul true_race hispanic rowindex 1 __name Torres Raul asian_mean 0.081465 asian_std 0.038318 asian_lb 0.032789 asian_ub 0.034667 hispanic_mean 0.646059 hispanic_std 0.144663 hispanic_lb 0.188246 hispanic_ub 0.219772 nh_black_mean 0.037737 nh_black_std 0.045439 nh_black_lb 0.006477 nh_black_ub 0.006603 nh_white_mean 0.076125 nh_white_std 0.059213 nh_white_lb 0.019312 nh_white_ub 0.020782 other_mean 0.158614 other_std 0.03918 other_lb 0.081994 other_ub 0.083105 race hispanic Name: 1, dtype: object
pred_nc_reg_name(df, namecol, num_iter=100, conf_int=0.9)
What it does:
Removes extra space.
Uses the full name NC model to predict the race and ethnicity.
Parameters
df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred
namecol : {string, list} string or list of the name or location of the column containing the first name, last name, middle name, and suffix, if there. The first name and last name columns are required. If no middle name of suffix columns are there, it is assumed that there are no middle names or suffixes.
num_iter : int, default=100 number of iterations to calculate uncertainty in model
conf_int : float, default=0.9 confidence interval in predicted class
Output: Appends the following columns to the pandas DataFrame or CSV: race + ethnicity. The codebook is here. For each race it will provide the mean, standard error, lower & upper bound of confidence interval
>>> import pandas as pd >>> names = [ ... {"last": "hernandez", "first": "hector", "true_race": "HL+O"}, ... {"last": "zhang", "first": "simon", "true_race": "NL+A"}, ... ] >>> df = pd.DataFrame(names) >>> from ethnicolr import pred_nc_reg_name >>> odf = pred_nc_reg_name(df, 'last','first') ['HL+A', 'HL+B', 'HL+I', 'HL+M', 'HL+O', 'HL+W', 'NL+A', 'NL+B', 'NL+I', 'NL+M', 'NL+O', 'NL+W'] >>> odf last first true_race __name rowindex HL+A_mean HL+A_std HL+A_lb HL+A_ub HL+B_mean ... NL+M_ub NL+O_mean NL+O_std NL+O_lb NL+O_ub NL+W_mean NL+W_std NL+W_lb NL+W_ub race 0 hernandez hector HL+O Hernandez Hector 0 0.000054 0.000354 5.833132e-10 4.291366e-09 0.009606 ... 0.000416 0.090123 0.036310 0.000705 0.003757 0.021228 0.021222 0.000368 0.001230 HL+O 1 zhang simon NL+A Zhang Simon 1 0.000603 0.002808 1.988648e-07 2.766486e-07 0.000026 ... 0.000086 0.125159 0.042818 0.050547 0.057208 0.003149 0.005437 0.000210 0.000225 NL+A [2 rows x 54 columns] >>> odf.iloc[0] last hernandez first hector true_race HL+O __name Hernandez Hector rowindex 0 HL+A_mean 0.000054 HL+A_std 0.000354 HL+A_lb 0.0 HL+A_ub 0.0 HL+B_mean 0.009606 HL+B_std 0.040739 HL+B_lb 0.0 HL+B_ub 0.000003 HL+I_mean 0.001605 HL+I_std 0.004569 HL+I_lb 0.0 HL+I_ub 0.0 HL+M_mean 0.147628 HL+M_std 0.215733 HL+M_lb 0.001253 HL+M_ub 0.001297 HL+O_mean 0.36902 HL+O_std 0.132249 HL+O_lb 0.002289 HL+O_ub 0.019187 HL+W_mean 0.264246 HL+W_std 0.090536 HL+W_lb 0.001782 HL+W_ub 0.015628 NL+A_mean 0.012004 NL+A_std 0.010873 NL+A_lb 0.000121 NL+A_ub 0.000281 NL+B_mean 0.010891 NL+B_std 0.01404 NL+B_lb 0.000094 NL+B_ub 0.000383 NL+I_mean 0.005182 NL+I_std 0.008259 NL+I_lb 0.000009 NL+I_ub 0.000068 NL+M_mean 0.068412 NL+M_std 0.08564 NL+M_lb 0.000172 NL+M_ub 0.000416 NL+O_mean 0.090123 NL+O_std 0.03631 NL+O_lb 0.000705 NL+O_ub 0.003757 NL+W_mean 0.021228 NL+W_std 0.021222 NL+W_lb 0.000368 NL+W_ub 0.00123 race HL+O Name: 0, dtype: object
Application
To illustrate how the package can be used, we impute the race of the campaign contributors recorded by FEC for the years 2000 and 2010 and tally campaign contributions by race.
Data on race of all the people in the DIME data is posted here The underlying python scripts are posted here
Data
In particular, we utilize the last-name–race data from the 2000 census and 2010 census, the Wikipedia data collected by Skiena and colleagues, and the Florida voter registration data from early 2017.
Evaluation
SCAN Health Plan, a Medicare Advantage plan that serves over 200,000 members throughout California used the software to better assess racial disparities of health among the people they serve. They only had racial data on about 47% of their members so used it to learn the race of the remaining 53%. On the data they had labels for, they found .9 AUC and 83% accuracy for the last name model.
Evaluation on NC Data: https://github.com/appeler/nc_race_ethnicity
Contributor Code of Conduct
The project welcomes contributions from everyone! In fact, it depends on it. To maintain this welcoming atmosphere, and to collaborate in a fun and productive way, we expect contributors to the project to abide by the Contributor Code of Conduct.
License
The package is released under the MIT License.
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