student2 {bnRep} | R Documentation |
student Bayesian Networks
Description
A survey on datasets for fairness-aware machine learning.
Format
A discrete Bayesian network modeling students' achievement in the secondary education of two Portuguese schools in 2005–2006 in the Mathematics subject. The DAG was taken from the referenced paper and the probabilities learned from the associated dataset. The vertices are:
- activities
Extra-curricular activities (yes, no);
- address
Student's home address type (Rural, Urban);
- age
Student's age (15, 16, 17, ..., 22);
- class
Final grade (< 10, >= 10);
- failures
Number of past class failures (0, 1, 2, 3);
- famsize
Race (non-white, white);
- famsup
Family size (Less or equal to 3, Greater than 3);
- Fedu
Father's education (None, Primary Education, 5th to 9th Grade, Secondary Education, Higher Education);
- Fjob
Father's job (At Home, Healthcare Related, Other, Civil Services, Teacher);
- G1
First period grade (< 10, >= 10);
- G2
Second period grade (< 10, >= 10);
- goout
Going out with friends (Very Low, Low, Medium, High, Very High);
- guardian
Student's guardian (Mother, Father, Other);
- higher
Wants to take higher education (yes, no);
- internet
Internet access at home (yes, no);
- Medu
Mother's education (None, Primary Education, 5th to 9th Grade, Secondary Education, Higher Education);
- Mjob
Mother's job (At Home, Healthcare Related, Other, Civil Services, Teacher);
- nursery
Attended nursery school (yes, no);
- paid
Extra paid classes within the course subject (yes, no);
- Pstatus
Parent's cohabitation status (Living together, Apart);
- reason
Reason to choose this school (Close to Home, School Reputation, Course Preference, Other);
- romantic
With a romantic relationship (yes, no);
- school
Student's school (Gabriel Pereira, Mousinho da Silveira);
- schoolsup
Extra educational support (yes, no);
- sex
Student's sex (Female, Male);
- traveltime
Home to school travel time (Less than 15min, 15 to 30 mins, 30 mins to 1 hour, More than 1 hour);
Value
An object of class bn.fit
. Refer to the documentation of bnlearn
for details.
References
Le Quy, T., Roy, A., Iosifidis, V., Zhang, W., & Ntoutsi, E. (2022). A survey on datasets for fairness-aware machine learning. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 12(3), e1452.