Model ensembling is a term from the fields of artificial intelligence and big data. It describes a method in which several different models work together to achieve better results than a single model alone.
Imagine asking three weather services whether it will rain tomorrow. Each service uses its own methods and has different strengths. If you take the results of all three into account (e.g. according to the majority principle), the forecast is usually more reliable than the opinion of just one service. This is exactly what happens with model ensembling: different algorithms, which were previously trained individually, combine their predictions to minimise errors and become more accurate.
This makes model ensembling particularly valuable when data can be interpreted differently or individual models could be wrong. In practice, this technology is used in lending, for example: Several AI models analyse the creditworthiness of an applicant and the combined assessment provides a particularly robust decision.
Model ensembling therefore increases the quality of predictions and helps companies to make safer and more informed decisions.
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