Fault Prediction -- pre-release name
The following are steps to install.
- Install virtual environment. (Optional but recommended)
- Then run the following:
$ cd fault_prediction # Basically set cwd to fault_prediction $ pip install -e . $ python -m tests.test _test filename
- filename just "ant"
- 6 Learners [KNN, NB, SVM, LR, DT, RF]
- with smote and without smote, 5x5 cross val
- only 14 datasets, other datasets from openscience were very small.
- compared against 4 measures, [Precision, recall, accuracy, f_score]
- Considering all the measure almost all learners performed the best. But in most cases we saw, Random forest to win.
- Smote is needed for highly imbalanced classes.
- Runtimes are within 5-20 mins for each dataset with 6 learners repeating 25 times. Only 2 datasets took about 2hours each.