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- W4385202314 abstract "Stacking is a novel broad technique to meta-learning. Stacking is a technique for combining the predictions of several heterogeneous categorization models (Liu et al. in A stacked generalization ensemble model for optimization and prediction of the gas well rate of penetration: a case study in Xinjiang, 2021). Stacking is used to achieve universal accuracy. Unlike other methods of combining, (e.g., boosting, bagging) (Dietterich in Multiple Classifier Systems, Springer, Berlin, Heidelberg, 2000), in stacking, when the original training data has been transformed into numerous subsets known as bootstraps, we send the entire dataset to base learners. How to use stacking to enhance a single generalizer is also covered in detail. Performance has increased, according to two trials. As a level-1 generalizer, HERBIE’s metrics are utilized in the first experiment with a single generalizer. The process of stacking entails fitting multiple distinct kinds of models on the same data and uses another model to learn how to integrate the predictions in the most effective way possible." @default.
- W4385202314 created "2023-07-25" @default.
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- W4385202314 date "2023-01-01" @default.
- W4385202314 modified "2023-09-23" @default.
- W4385202314 title "Ensemble Learning Method Using Stacking with Base Learner, A Comparison" @default.
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- W4385202314 doi "https://doi.org/10.1007/978-981-99-3878-0_14" @default.
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