Description: Feature combination is a fundamental process in the field of automated machine learning (AutoML) that involves merging multiple features or variables into a new feature. This approach aims to enhance the performance of predictive models by capturing complex interactions and relationships between the original variables. By creating new features, patterns that might otherwise go unnoticed can be highlighted, allowing machine learning algorithms to learn more effectively. This process can include simple mathematical operations, such as addition or multiplication, as well as more complex transformations, such as applying nonlinear functions. Feature combination is particularly valuable in high-dimensional datasets, where dimensionality reduction and the creation of new variables can facilitate interpretation and analysis. In summary, this technique not only optimizes model performance but also contributes to a better understanding of the underlying data, which is crucial for informed decision-making across various technological fields.
