Description: Heterogeneous ensemble learning is a technique that combines multiple machine learning algorithms to improve the performance of predictive models. This methodology is based on the idea that different algorithms can capture distinct patterns in the data, allowing for greater robustness and accuracy in predictions. By integrating diverse models, the aim is to mitigate the weaknesses of each and leverage their strengths, resulting in a more generalizable and effective model. The main characteristics of heterogeneous ensemble learning include the diversity of the models used, the combination of their predictions through techniques such as majority voting or weighted averaging, and the ability to adapt to various types of data and problems. This technique is particularly relevant in contexts where the complexity of the data is high and where a single model may not be sufficient to capture all present variations. In summary, heterogeneous ensemble learning represents a collaborative approach in machine learning, where the synergy between different algorithms can lead to significantly better results than those obtained using a single model.
