Ensemble Functionality for Model Development

[TinaChen95]

Is your feature request related to a problem? Please describe. Yes, the problem is that current models may not always provide accurate predictions, especially when dealing with complex and diverse datasets. As a result, users may need to manually intervene and adjust the predictions or use multiple models to obtain more reliable results.

Describe the solution you'd like I would like to request the addition of an ensemble feature to the model development process. Specifically, I would like the ability to randomly split data and train multiple sub-models. Or maybe I want to use multiple models provided by community. During the model usage stage, input data will generate multiple predictions, which can be fused together to obtain the final prediction. This ensemble approach can improve model accuracy and robustness, and reduce the need for manual intervention.

Describe alternatives you've considered One alternative solution would be to manually train multiple models and combine their predictions using an external tool or script. However, this would be time-consuming and require a lot of expertise, especially for users without a technical background.

Additional context I believe that adding an ensemble feature to the model development process can greatly enhance the model's performance and user experience. This feature could be implemented in a variety of ways, such as incorporating different algorithms, hyperparameters, or data splits. It would also be helpful to provide users with options to customize the ensemble process, such as specifying the number of sub-models to train or the fusion method to use.