The term "approximation methods in ML" belongs to the category of artificial intelligence as well as big data and smart data. Such methods are used in the field of machine learning (ML) to recognise patterns and correlations in very large or complex data sets better and faster.
Approximation methods help to simplify complicated calculations by finding a good approximation rather than calculating every last detail exactly. This saves time and computing power. As a result, companies can analyse data more quickly and, for example, create forecasts about future sales figures or trends.
An illustrative example: Imagine an online shop wants to use millions of pieces of customer data to find out which products will be in demand next season. As it would be too time-consuming to consider all the data individually, the company uses approximation methods in ML to recognise typical purchasing patterns. The result is a reliable prediction of which items they should have in stock.
Approximation methods in ML therefore make it possible to gain useful insights from "big data" and make smart decisions without getting lost in the details.
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