Cross-validation is a term that plays a particularly important role in the fields of artificial intelligence, big data, smart data and digital transformation.
Cross-validation is a method in which a computer-aided model - for example an artificial intelligence that is supposed to analyse images or texts - is tested in a very specific way. The aim is to find out how good this model really is and how reliably it also works with new, unknown data.
Imagine you have a large table of data, such as thousands of customer entries. Instead of training the model with just one part of this data and testing it with the other, cross-validation involves splitting the entire amount of data several times in different ways. Each time, the model trains with one part and then tests its performance on the remaining part. This gives a much more accurate picture of how reliable the model is in practice.
Cross-validation can prevent a model from being assessed too "optimistically" because it happens to fit a particular data set particularly well. Companies use this method to make better decisions when developing artificial intelligence and data-based solutions.
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