Overfitting is a term from the fields of Artificial Intelligence, Big Data and Smart Data, and Digital Transformation. It describes a common problem that occurs when developing models for data analysis or machine learning.<\/p>\n
Imagine an artificial intelligence is meant to recognise whether a picture shows a cat using many photos. With overfitting, the AI memorises too many details from the training images, such as the sofa in the background or the colour of the blanket \u2013 unimportant details that only appear in the training data. As a result, the model works excellently on the learned images, but on new, unknown photos, it suddenly no longer reliably recognises cats because it pays attention to the wrong clues.<\/p>\n
Overfitting often occurs when a model is too complex or too few training data are used. The goal is therefore to learn the right patterns without getting lost in unimportant details.<\/p>\n
Overfitting can be detected when results are very good on known data but suddenly much worse on new data. To avoid overfitting, experts use various methods, such as more training data or specific techniques that simplify the model.<\/p>","protected":false},"excerpt":{"rendered":"