👉 In artificial intelligence, "retroposition" refers to a technique used in machine learning where an algorithm is trained on data that is not representative of its intended use. This can lead to biased or inaccurate predictions when applied to real-world tasks. This technique occurs when training data is created using techniques such as oversampling, undersampling, and feature engineering, which are designed to artificially increase the size of the dataset while keeping it diverse enough to prevent bias in the resulting model. However, if this training
