👉 Odds engineering is a method used in machine learning and data science where the probability estimates for different outcomes are adjusted to influence model predictions, particularly in scenarios involving imbalanced datasets. By manipulating these odds, engineers can steer the model's decision-making process to favor less frequent but potentially more important outcomes. This is achieved through techniques like adjusting class weights, modifying decision thresholds, or employing custom loss functions that penalize certain misclassifications more heavily. The goal is to improve the model's performance on minority classes, making it more balanced and effective in real-world applications where certain outcomes are rarer but crucial.
