Data imbalance is an important term in the fields of artificial intelligence, big data and smart data, as well as automation. Data imbalance means that in a dataset, individual groups or categories occur much more frequently than others. For example, when analysing customer satisfaction, the number of satisfied customers may be significantly larger than that of unsatisfied customers. This leads to artificial intelligence and other data models drawing incorrect conclusions or overlooking certain groups.<\/p>\n
A clear example: Imagine you want to automatically sort emails. Out of 1,000 emails, 950 are marked as \u201enormal\u201c and only 50 as \u201espam\u201c. A system that hardly learns spam will end up classifying too many spam emails as normal. This is because the rare cases in the dataset (in this instance: spam) have too little weight during evaluation.<\/p>\n
Data imbalance is a particular consideration when developing automation and AI solutions. Only when the data is as balanced as possible can a model operate reliably and fairly. Therefore, it is important to pay attention to and compensate for data imbalance during data collection and evaluation.<\/p>","protected":false},"excerpt":{"rendered":"