fit_tr_val's train_test_split had no random_state, so val_split=float picked a different split every run; occasionally the split produced a posterior distribution that made abstention's temperature-scaling L-BFGS optimizer diverge to NaN. Threaded random_state through the base class and all four calibrator subclasses, and pinned the test to a seed confirmed stable across repeated runs. |
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|---|---|---|
| .. | ||
| __init__.py | ||
| calibration.py | ||
| methods.py | ||
| neural.py | ||
| svmperf.py | ||