Description: A heterogeneous feature set refers to the inclusion of diverse features coming from different types or sources in a machine learning model. These features can encompass numerical, categorical, textual, and image data, among others, allowing for a richer and more complex representation of information. Heterogeneity in features is crucial, as each type of data can provide valuable and unique insights that, when combined, enhance the model’s ability to learn patterns and make predictions. This approach is particularly relevant in contexts where data is varied and comes from multiple sources, such as in general machine learning applications. Integrating a heterogeneous feature set enables AutoML models to better adapt to the complexity of the real world, where data is not homogeneous and requires specialized treatment to extract its maximum potential.
