Data Science
Feature and Model Selection
1 credits
Performance of a classification model. Generalization error, overfitting of training data. Shrinkage, feature selection, Akaike Information Criterion, Bayesian Information Criterion. k-fold cross validation. Bootstrapping. Receiver Operating Characteristic curve. Elastic nets, regularization.
Average
87.8%
Students
914
Sections
10
Grade distribution. <50: 1, 50-54: 0, 55-59: 0, 60-63: 3, 64-67: 7, 68-71: 17, 72-75: 33, 76-79: 64, 80-84: 122, 85-89: 200, 90-100: 456.
Historical Averages
2016–2024
Lowest section avg
81.6%
Highest section avg
91.7%
Terms offered
9
Some grade ranges were withheld by UBC to protect student privacy and are not shown above.
Grade data from ubc-pair-grade-data.
| Section | Activity | Status | Time | Instructor |
|---|---|---|---|---|
| 001 | Lecture | Open | Mon 09:30-11:00, Wed 09:30-11:00 | Prajeet Bajpai |
| 002 | Lecture | Open | Tue 08:00-09:30, Thu 08:00-09:30 | Elham E Khoda |
| L01 | Laboratory | Open | Mon 14:00-16:00 | Prajeet Bajpai |
| L02 | Laboratory | Open | Wed 14:00-16:00 | Prajeet Bajpai |
| L03 | Laboratory | Open | Tue 14:00-16:00 | Elham E Khoda |
| L04 | Laboratory | Open | Thu 14:00-16:00 | Elham E Khoda |
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