StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performa
論文傳送門 在機器學習(ML)中，ensemble方法，如bagging、boosting和stacking，是廣泛建立的方法，通常可以實現頂級的預測性能。stack(也稱爲「stack generalization」)是一種集成方法，它組合了至少在一層中排列的異構基礎模型，然後使用另一個元模型來總結這些模型的預測。雖然這可能是一種提高最大似然估計預測性能的高效方法，但是從頭生成一堆模型可能是一個
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相關文章
- 1. A Tour of Machine Learning Algorithms
- 2. A Comprehensive Analysis of Sequence Alignment Algorithms for LongRead Sequencing
- 3. Data Mining: Practical Machine Learning Tools and Techniques
- 4. Discriminative Embeddings of Latent Variable Models for Structured Data
- 5. Ensemble Learning
- 6. Ensemble Learning (Bagging,boosting,Blending,Stacking)
- 7. The Implementation of baseline algorithms of Machine Learning
- 8. Learning Machine Learning, Part 2: Algorithms and Techniques
- 9. problem-solving-with-algorithms-and-data-structure-using-python
- 10. Deep Learning Models and Code for Pose Estimation