You have analyzed a model and found what looks like a meaningful improvement. Before presenting it, you need to validate that the result is stable across data splits, holds on recent data, and still makes sense at the chosen operating threshold.
Cross-validation for result stabilityThreshold tuning tradeoffsCalibration checks for probability qualityConfusion matrix review and business translation