Description: Instance selection is the process of choosing a representative subset of instances from a larger dataset, with the aim of using them for training or testing supervised learning models. This process is crucial because the quality and representativeness of the selected instances can significantly influence the model’s performance. When selecting instances, the goal is to balance the diversity and relevance of the data, ensuring that the model learns from examples that adequately cover the feature space. Additionally, instance selection can help reduce training time and improve computational efficiency, especially in large datasets. There are various techniques to carry out this selection, ranging from random methods to more sophisticated algorithms that consider the similarity between instances or the importance of each in the context of the problem being solved. In summary, instance selection is a fundamental stage in the modeling process, allowing for optimized data usage and improved predictive capability of machine learning models.
