Optimizing Ensemble Weights and Hyperparameters of Machine Learning Models for Regression Problems

Understand

Aggregating multiple learners through an ensemble of models aim to make better predictions by capturing the underlying distribution of the data more accurately.

• Different ensembling methods, such as bagging, boosting, and stacking/blending, have been studied and adopted extensively in research and practice.
• While bagging and boosting focus more on reducing variance and bias, respectively, stacking approaches target both by finding the optimal way to combine base learners.
• In stacking with the weighted average, ensembles are created from weighted averages of multiple base learners.