metaensemble
A metaensemble is an ensemble learning approach where multiple base models generate predictions that are then combined by a secondary model called a meta-model. The goal is to improve predictive performance by leveraging complementary strengths of diverse learners.
Common procedures include stacking (stacked generalization) and blending. In stacking, predictions of base models are collected
Base models can be of different types (e.g., decision trees, logistic regression, neural networks) and the inputs
Advantages include improved accuracy, robustness, and the ability to combine heterogeneous models. Limitations include risk of
Applications include Kaggle competitions, production forecasting, and any scenario where model diversity can be exploited. Related