Hyperparameter tuning is a key term in the fields of artificial intelligence and big data. It refers to the fine-tuning of settings ("hyperparameters") in computer programmes that are designed to solve problems using machine learning.
Imagine you want to train an artificial intelligence to automatically categorise emails into "important" and "unimportant". Your computer programme has various settings, for example how often it runs through data or how strongly it reacts to errors. These settings are called hyperparameters. As with a recipe, you have to choose the right ingredients and quantities so that the end result is convincing.
With hyperparameter tuning, you systematically try out different settings until the artificial intelligence shows the best possible performance. It's similar to baking: Sometimes you use more sugar, sometimes less, until the cake tastes the best.
Correct hyperparameter tuning saves time, ensures better results and makes artificial intelligence applications in areas such as spam detection or predictions more accurate. Especially with big data, i.e. very large amounts of data, this method is crucial for achieving useful results.
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