We investigate post-softmax calibration for weightedensemble voting in RF signal classification. Neural network confidence scores are often miscalibrated, leading to overconfidentpredictions that degrade ensemble performance. Using per-modeltemperature scaling, we reduce Expected Calibration Error(ECE) from 15.4% to 4.2% (73% improvement) and improveutility (accuracy × coverage) from 65.6% to 71.7% (+9.3%)at τ = 0.6 with <0.1ms inference … Continue reading Confidence Calibration for Weighted Voting in RF Ensembles