high-analytical-value-v1 / threshold.json
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Replace model with finetune_api_labels_xlmr_large_final_run_20261008_094810 (threshold 0.5)
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{
"threshold": 0.5,
"rule": "predict HAV when P(HAV) >= threshold",
"positive_label": "HAV",
"max_seq_length": 256,
"chosen": "manually",
"updated": "2026-10-08",
"tuned_threshold_info": {
"threshold": 0.873214840888977,
"rule": "predict HAV when P(HAV) >= threshold",
"positive_label": "HAV",
"recall_floor": 0.75,
"tuned_on": "pooled out-of-fold predictions, 5-fold CV on all labelled data (n=14982)",
"final_mode": "cv_full",
"oof_pooled_at_threshold": {
"precision": 0.8600739371534196,
"recall": 0.7502418574653338,
"f1": 0.8014123320702722,
"f0.5": 0.8356080741326054,
"roc_auc": 0.9286660504841191
},
"oof_pooled_at_0.5": {
"precision": 0.8010415071220708,
"recall": 0.8432763624637214,
"f1": 0.8216165265886419,
"f0.5": 0.8091466056067826,
"roc_auc": 0.9286660504841191
},
"cv_fold_mean_at_threshold": {
"precision": 0.8602713115017393,
"recall": 0.7502417405318292,
"f1": 0.8014351606206255,
"f0.5": 0.8357112536510138,
"roc_auc": 0.9305891130964014
},
"cv_fold_sd_at_threshold": {
"precision": 0.01264469910857227,
"recall": 0.004401753404119227,
"f1": 0.0035358058313396264,
"f0.5": 0.008582361435463492,
"roc_auc": 0.004316468755994513
}
}
}