👉 Exclusion engineering is a design strategy used in machine learning and artificial intelligence systems to prevent certain data points, often deemed outliers or irrelevant, from significantly impacting the model's learning process. This technique involves intentionally removing or masking these problematic instances to ensure that the model focuses on learning from the majority, representative data. By doing so, exclusion engineering helps in reducing noise and bias, leading to more robust and reliable models that generalize better to unseen data. It is particularly useful in scenarios where the dataset may contain anomalies or when certain features are known to be unreliable.
