Difference between revisions of "Latino/Latina/Latinx/Hispanic Learners in North America"
Jump to navigation
Jump to search
| Line 3: | Line 3: | ||
* False negatives rates were greater for Latino students when Decision Tree and Random Forest yielded was used | * False negatives rates were greater for Latino students when Decision Tree and Random Forest yielded was used | ||
* White students had higher false positive rates across all models, Decision Tree, SVM, Logistic Regression, Random Forest, and SGD | * White students had higher false positive rates across all models, Decision Tree, SVM, Logistic Regression, Random Forest, and SGD | ||
Lee and Kizilcec (2020) [[https://arxiv.org/pdf/2007.00088.pdf pdf]] | |||
* Models predicting college success (or median grade or above) | |||
* Random forest algorithms performed significantly worse for underrepresented minority students (URM; American Indian, Black, Hawaiian or Pacific Islander, Hispanic, and Multicultural) than non-URM students (White and Asian) | |||
* The fairness of the model, namely demographic parity and equality of opportunity, as well as its accuracy, improved after correcting the threshold values | |||
Revision as of 19:28, 22 March 2022
Anderson et al. (2019) pdf
- Models predicting six-year college graduation
- False negatives rates were greater for Latino students when Decision Tree and Random Forest yielded was used
- White students had higher false positive rates across all models, Decision Tree, SVM, Logistic Regression, Random Forest, and SGD
Lee and Kizilcec (2020) [pdf]
- Models predicting college success (or median grade or above)
- Random forest algorithms performed significantly worse for underrepresented minority students (URM; American Indian, Black, Hawaiian or Pacific Islander, Hispanic, and Multicultural) than non-URM students (White and Asian)
- The fairness of the model, namely demographic parity and equality of opportunity, as well as its accuracy, improved after correcting the threshold values