Introduction Model tuning and estimation has evolved from simple extrapolation to sophisticated probabilistic modeling frameworks. In contemporary data science, decision-makers require more than estimates, the need for clear statements about likelihood, risk, and uncertainty are critical to correct decision-making. However, despite the advances in predictive modeling, there is a persistent limitation of making quick and robust decisions from the estimated probabilities. The estimates are often not meaningfully interpreted. This is where the odds_summary function becomes strategically important.