The term Dataset Shift originates from the fields of Artificial Intelligence, Big Data and Smart Data, and Digital Transformation. Dataset Shift describes a change in the data that, for example, artificial intelligences or algorithms work with. This means that the data used to develop and train models suddenly differs from the data that the system receives later in operation.<\/p>\n
A simple real-world example: A company develops a system for detecting fraudulent transactions in online retail. The system is trained on data from recent years. But suddenly, customer behaviour changes, for example, because many people switch to a particular payment method due to a new trend. However, the previous data does not describe this new behaviour. As a result, the model makes incorrect decisions more frequently.<\/p>\n
Dataset shift can cause AI models or data analyses to become less reliable or even produce completely incorrect results. Therefore, companies must regularly check whether their models still work correctly with new, changed data. This is the only way to utilise the full potential of artificial intelligence and big data and avoid costly wrong decisions.<\/p>","protected":false},"excerpt":{"rendered":"