Description: Heterogeneous ensemble is an approach in data science and machine learning that combines different types of models to improve the overall performance of predictions. This method is based on the premise that by integrating diverse models, each with its own strengths and weaknesses, greater accuracy and robustness in results can be achieved. Unlike homogeneous ensemble methods, which use multiple instances of the same type of model, heterogeneous ensemble incorporates a variety of algorithms, such as decision trees, support vector machines, and neural networks, among others. This diversity allows the system to leverage the unique characteristics of each model, resulting in better generalization and reduced overfitting. Additionally, heterogeneous ensemble can be particularly useful in situations where data is complex or exhibits significant variations, as different models can capture different patterns in the data. In the context of machine learning, this approach can combine distinct model architectures to address specific tasks, such as image classification or pattern recognition, thereby enhancing the overall effectiveness of the system.
