A new implementation of an adiabatically-trained ensemble model is derived that shows significant improvements over classical methods. In particular, empirical results of this new algorithm show that it offers not just higher performance, but also more stability with less classifiers, an attribute that is critically important in areas like explainability and speed-of-inference. In all, the empirical analysis displays that the algorithm can provide an increase in performance on unseen data by strengthening stability of the statistical model through further minimizing and balancing variance and bias, while decreasing the time to convergence over its predecessors.
翻译:一种经过边际训练的混合模型的新实施过程表明,与古典方法相比有了显著的改进。 特别是,这一新算法的经验结果表明,它不仅提高了性能,而且更稳定地使用分类方法,这种特性在可解释性和推断速度等方面至关重要。 总之,实证分析表明,通过进一步减少和平衡差异和偏差,同时缩短与先前的趋同时间,通过进一步减少和平衡统计模型的稳定性,使算法能够提高隐蔽数据的性能。