👉 Discrimination engineering is a technique used in machine learning to improve the performance of models, particularly in classification tasks, by enhancing their ability to distinguish between different classes. It involves modifying the model's decision boundary or adjusting its parameters to better capture the differences between classes while minimizing overlap. This can be achieved through various methods, such as adjusting class weights, using different loss functions, or applying feature engineering to highlight discriminative attributes. By refining how the model interprets and separates data points, discrimination engineering aims to reduce misclassification errors and improve overall classification accuracy.
