Here is an example of ensemble learning using R-based scripts together with Python-based Data Version Control tool to demonstrates the potential of building repeatable and reusable production-ready ML pipelines for R-based applications by dealing with the following challenges (for Kaggle competition regarding wine sales prediction):
- Ability to conditionally trigger execution of 3 different ML prediction models (Linear Regression, GBM, and XGBOOST)
- Ability to conditionally trigger model ensemble prediction based on predictions of those 3 individual models
- Ability to specify weights of each of the individual model predictions in the ensemble