The text describes the concept and process of building stacked ensembles in machine learning using H2O.ai and Optuna. The author outlines the steps involved in training a stacked ensemble, including the training of base models such as Deep Neural Networks, XGBoost, and LightGBM, and subsequently training the meta-model using H2OStackedEnsembleEstimator. The summary provides an in-depth understanding of the process involved in creating a stacked ensemble for predictive modeling.
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Stacked Ensembles for Advanced Predictive Modeling With H2O.ai and Optuna
What are Stacked Ensembles?
A stacked ensemble combines predictions from multiple models through another, higher-level model, with the aim being to increase overall predictive performance by capitalizing on the unique strengths of each constituent model.
How to Train Stacked Ensembles with H2O.ai
First, import the necessary libraries and initialize the H2O cluster. Then, load in the dataset and prepare the data for modeling using H2O. Next, train the base models, including Deep Neural Networks, XGBoost, and LightGBM, using H2O’s estimators. Finally, train the meta-model using the base models and optimize its hyperparameters using optuna.
Comparing Performance: Stacked Ensemble Versus Standalone Base Models
The stacked ensemble produced an RMSE of 0.31 on the validation set, while the best-performing standalone base model achieved an RMSE of 0.35. This means that Stacking was able to improve predictive performance by 11% on unseen data!
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