Ensemble learning is a term used in artificial intelligence and in the fields of big data and smart data. It involves several different "models" - i.e. programmes that learn from data - working together to achieve better results than a single model alone.
Think of Ensemble Learning as a team of experts working together to make a decision. Each expert contributes their own opinion, and together you often arrive at a better solution than you would alone. In artificial intelligence, this principle is used, for example, to make predictions about customer behaviour or to automatically recognise texts.
A simple example: Suppose you want to use artificial intelligence to predict whether a customer will buy a product. A single model could be wrong. However, if you use several models that "think" differently, they can vote. The majority decides and the prediction becomes more reliable.
Ensemble learning therefore ensures that computer systems work more precisely and make fewer errors. This method is particularly useful in areas where a lot of data needs to be analysed.
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