Model ensembling is a term from the fields of Artificial Intelligence and Big Data. It describes a method where multiple different models work together to achieve better results than a single model alone.
Imagine you ask three weather services if it will rain tomorrow. Each service uses its own methods and has different strengths. If you consider the results of all three (e.g., using a majority vote), the prediction is usually more reliable than the opinion of just one service. This is exactly what happens with model ensembling: various algorithms, which were 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 might err. In practice, this technique is used, for example, in credit lending: several AI models analyse an applicant's creditworthiness, and the combined assessment delivers 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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